Data Modelling Consultant Jobs

About the Job: LaunchDarkly is looking for a Data Analyst to join our Data & Analytics team in either India or Ireland. In this role, you will focus on building and scaling reliable data models, automating reporting, and supporting global analytics delivery. You will partner closely with the Senior Data Analyst (Revenue & Metrics), Data Engineering, and business stakeholders to translate business logic into clean, scalable datasets. This role plays a key part in ensuring that defined metrics (e.g., ARR, NRR ) are consistently implemented, automated, and delivered across the organization. Responsibilities: Data Modeling & Transformation Build and maintain scalable data models using modern tools (e.g., Snowflake, dbt). Translate defined business logic (e.g., ARR calculations) into clean and reusable datasets. Metrics Implementation Implement and maintain standardized metrics (e.g., ARR, NRR , churn) in the data layer. Ensure consistency across dashboards and reports. Automation & Pipeline Support Work with Data Engineering to automate data pipelines and reduce manual reporting processes. Dashboarding & Reporting Develop and maintain dashboards that provide consistent and reliable visibility into business performance. Data Quality & Validation Validate data outputs, identify inconsistencies, and support reconciliation efforts across systems (e.g., Salesforce, billing systems). Global Collaboration Partner with teams across the US and EMEA to deliver timely and high-quality analytics outputs. Qualifications: Typically requires a minimum of 5 years of related experience in data analytics, business intelligence, or analytics engineering Strong SQL skills and experience working with structured and large datasets Experience with modern data tools (e.g., Snowflake, dbt, Looker or similar) Experience building and maintaining data models and dashboards Experience implementing business logic into data transformations Strong attention to detail and abilit

At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive. The Safety and Customer Care (SCC) team at Lyft manages over 1.7 million monthly human and AI interactions and serves as Lyft's primary direct touchpoint with riders and drivers. We handle critical infrastructure that powers both human associates and AI agents to make riders and drivers feel safe and comfortable while riding or driving with Lyft, transforming every support interaction into a moment of genuine connection. As a Data Engineer on the SCC team, you will have ownership over the data modeling and pipelines that power SCC’s Associate and AI Agent Platform . Your efforts will be critical to the reliability of our pipelines, execution of third party data integrations, accurate reporting of agents performance, and efficiency improvements that can save millions of dollars / year. You will work cross-functionally to bridge Lyft's business goals with data engineering. Your efforts will allow access to business and user behavior insights, using huge amounts of Lyft data to fuel several teams such as Analytics, Data Science, Engineering, and many others. Responsibilities: Owner of the core data pipeline, responsible for scaling up data processing flow to meet the rapid data growth at Lyft Evolve data model and data schema based on business and engineering needs Implement systems tracking data quality and consistency Develop tools supporting self-service data pipeline management (ETL) SQL and MapReduce job tuning to improve data processing performance Write well-crafted, well-tested, readable, maintainable code Participate in code reviews to ensure code quality and distribute knowledge Collaborate cross-functionally with product, engineering, data science, and marketing teams to understand business problems and align on prioritization and solutions Experience: Bachelor's degree in Compute

The Data Analyst, Revenue Operations is responsible for turning data from multiple source systems into the reporting, models, and visualizations that power revenue, sales, and operations decision-making. Reporting into the Revenue Operations organization, this role combines hands-on SQL and data modeling with predictive segmentation, opportunity analysis, and dashboard development — and serves as a trusted reporting partner to teams across the business. You will work primarily within Revenue Operations while collaborating cross-functionally with Sales, Business and Technical Operations, Sales Enablement, Support, and Finance to get things done. About NMI NMI, a global leader in payment technologies, allows software and sales professionals to deliver payment innovations to their clients without having to build payment expertise in house. NMI enables our partners with choice, and challenges the one-size-fits-all approach to payments. You've probably used NMI in the last 24 hours without even realizing it. We're the platform that powers success for innovative tech created by SMBs, entrepreneurs and fintech startups. We're creative problem solvers who help visionaries smash through boundaries and think beyond what's possible so they can think about what's next. But we're not just built for the tech savvy. We democratize the latest payments technology so that everyone can realize the benefits of easy payments across the full spectrum of commerce. Our mission is to use our expertise to enable more payments, in more ways and more places. We provide ISOs, Fintech Innovators and Technologists the freedom to focus on what they do best, liberating them from restricted payment solutions and giving them access to the latest payment technology now and in the future. How We Work - Our Core Values * Be a Good Person, It's Simple. We treat everyone fairly and with respect. We lead with kindness, humility and integrity. We take the time to understand each other and our customers. And

Build something people love Wealthsimple is Canada’s leading financial innovator. The company offers a full suite of simple, sophisticated financial products across managed investing, do-it-yourself trading, cryptocurrency, tax filing, spending and saving. Wealthsimple currently serves more than 4 million Canadians and holds over $155 billion in assets under administration. The company was founded in 2014 by a team of financial experts and technology entrepreneurs, and is headquartered in Toronto, Canada. We're proud of what we've built — and we're just getting started. Read our Culture Manual and learn more about how we work . The Sales Data team is responsible for supporting the entire Commercial team through data modeling, pipelining, dashboarding, experimentation, analyses, and machine learning models. Our stakeholder teams are diverse, including Financial Planners & Advisors, Business Development Representatives, our AI Voice Agent team, Revenue Operations and Commercial Systems. We are looking for a Senior Data Scientist who has strong foundations in data modeling and machine learning to lead high priority initiatives, take ownership as a decision science expert, and help shape the direction of our Commercial team and our AI Voice Agent program. Some of your projects may include: modeling sales data, creating and improving reporting for business stakeholders, exploratory data analysis to improve our sales processes, and developing and maintaining ML models. Our models are used to solve real business problems and directly impact Wealthsimple’s north star metric. Therefore, experience with supporting sales is a bonus. In this role, you will have the opportunity to: Build and maintain new data models that are accurate and reliable Work closely with a wide range of stakeholders from Data Science, Commercial Leadership, Sales Operations, Product, Design, and Engineering Define, prototype, implement and improve ML models Design experiments to test hypotheses (e.

About the Job: LaunchDarkly is looking for a Data Analyst to join our Data & Analytics team in Ireland. In this role, you will focus on building and scaling reliable data models, automating reporting, and supporting global analytics delivery. You will partner closely with the Senior Data Analyst (Revenue & Metrics), Data Engineering, and business stakeholders to translate business logic into clean, scalable datasets. This role plays a key part in ensuring that defined metrics (e.g., ARR, NRR ) are consistently implemented, automated, and delivered across the organization. Responsibilities: Data Modeling & Transformation Build and maintain scalable data models using modern tools (e.g., Snowflake, dbt). Translate defined business logic (e.g., ARR calculations) into clean and reusable datasets. Metrics Implementation Implement and maintain standardized metrics (e.g., ARR, NRR , churn) in the data layer. Ensure consistency across dashboards and reports. Automation & Pipeline Support Work with Data Engineering to automate data pipelines and reduce manual reporting processes. Dashboarding & Reporting Develop and maintain dashboards that provide consistent and reliable visibility into business performance. Data Quality & Validation Validate data outputs, identify inconsistencies, and support reconciliation efforts across systems (e.g., Salesforce, billing systems). Global Collaboration Partner with teams across the US and EMEA to deliver timely and high-quality analytics outputs. Qualifications: Typically requires a minimum of 5 years of related experience in data analytics, business intelligence, or analytics engineering Strong SQL skills and experience working with structured and large datasets Experience with modern data tools (e.g., Snowflake, dbt, Looker or similar) Experience building and maintaining data models and dashboards Experience implementing business logic into data transformations Strong attention to detail and ability to ensure data

Key Responsibilities Enterprise Data Strategy & Client Engagement: Develop and maintain a comprehensive data architecture strategy that aligns with organizational and client business objectives. Serve as a key technical advisor for clients, translating business requirements into innovative data solutions. Build and maintain strong client relationships by providing expert guidance and managing expectations throughout project lifecycles. Data Modeling, Design & Engineering: Design and optimize both logical and physical data models to support enterprise-wide systems. Architect data warehousing solutions, overseeing the integration of data from multiple sources to enable robust business intelligence and analytics. Directly develop, test, and implement ETL processes and data pipelines, ensuring data quality, consistency, and performance. Technology Evaluation & Implementation: Evaluate emerging data technologies and tools to determine their fit within the existing architecture and potential for future scalability. Oversee the integration of new technologies into the enterprise data architecture, balancing innovation with risk management. Team Leadership & Hands-On Management: Lead cross-functional teams, providing mentorship and technical guidance to junior data engineers and architects. Maintain a hands-on approach by actively participating in coding, design sessions, and troubleshooting complex data issues. Ensure project milestones are met through effective resource management and team coordination. Performance, Security & Best Practices: Optimize data storage, retrieval, and processing performance across various systems. Collaborate with security teams to enforce data governance, compliance, and privacy standards. Establish and promote best practices in data management, data engineering, and architecture design. Documentation & Reporting: Develop and maintain comprehensive documentation covering data architecture designs, data flows, integrati

Apply early for priority consideration. Gallup has limited openings for this internship, and the position will be filled quickly. As a data engineering intern at Gallup, you’ll help build and modernize the data tools, infrastructure and pipelines that enable teams to work with data at scale. Alongside experienced technical leaders, you’ll put your Python and SQL skills to work on meaningful projects while learning best practices, experimenting with modern tools and seeing firsthand how strong data engineering supports better decisions. What You’ll Do During your internship, you will: Build foundational data tooling and infrastructure designed to scale as the volume and frequency of data grow Help modernize legacy data pipelines using Python, SQL and Gallup’s evolving data platform Troubleshoot data quality issues in large, imperfect datasets Apply data engineering practices to move, transform and validate data across systems Help build reliable, efficient data workflows Learn from senior technical leaders who will mentor you on engineering practices, tools and approaches Collaborate with engineers and interact directly with stakeholders to understand needs and deliver useful solutions Use AI-enabled development tools to improve the quality and efficiency of your work What You Need Enrollment in an associate, bachelor’s or graduate degree program in computer science, data engineering, information systems, statistics, math or a related field required Classroom or work experience writing SQL required, including joins, aggregations and troubleshooting query performance Classroom or work experience coding in Python for data processing, scripting or automation required Working knowledge of relational database and data modeling concepts, including schema design and dimensional or star-schema modeling, required Comfort using AI tools such as Claude, Claude Code or GitHub Copilot to support high-quality, efficient work required Understanding of ETL/ELT concepts and how data

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role As a Data Engineer supporting Billing & Payments Engineering, you’ll join the Finance Data Engineering team and build the foundation for accurate, scalable billing and revenue data across Anthropic. You’ll work closely with Billing Engineering, Finance and Accounting, as well as Product and other partners, to turn billing and payments data into reliable metrics, reporting and insights. You’ll make sure teams have trustworthy data that scales with our growth, and you’ll lead your own projects to enable self-serve insights that help teams make data-driven decisions. Key responsibilities Partner with Billing Engineering, Finance and Accounting to understand their data and reporting needs, and translate them into technical requirements Design, build and maintain data models and pipelines in dbt that transform raw billing, usage and payments data into canonical datasets Own revenue data accuracy and reconciliation, making sure billing, usage and revenue data tie out across systems Define success metrics for billing systems, and build the data models and monitoring to track them Set data integrity standards and SLAs for timely, accurate delivery of billing and revenue data Share on-call and data operations responsibilities for Finance Data Engineering pipelines Build dashboards and self-serve data products that track core metrics and deliver insights across the company Promote best practices for dbt, data modeling and SQL Influence the Billing Engineering roadmap from a data systems perspective Build a thorough understanding of our data models and data architecture Minimum qualifications Experience as a Data Engineer, Ana

Perplexity is AI for people who expect more. On the data team, that means building the systems that make our data reliable, understandable, and usable by both humans and AI. We're looking for an analytics engineer or data engineer who wants to build the foundation for an AI-native data organization. You'll design core data models, pipelines, semantic layers, data quality systems, governance practices, and warehouse workflows that power the entire company: helping teams make strategic decisions, operate the business, and move faster with trusted data. You'll also make sure those systems are secure, privacy-aware, and legible to AI agents, data scientists, and the rest of the company. This role is for someone who can operate at the boundary of analytics engineering, data engineering, data governance, and internal product. You care about dimensional modeling, dbt standards, cost-aware warehouse design, access controls, privacy, and the details that make data trustworthy. You also believe AI should make the data stack faster, easier to maintain, and more accessible across the company without weakening security or governance. What You'll Do Build the core data foundation - design and maintain high-quality data models, marts, and pipelines that make analysis fast, reliable, and reusable. Manage the data warehouse - help own warehouse architecture, environments, permissions, performance, cost, data lifecycle, and operational hygiene so the platform scales cleanly. Make the warehouse AI-readable - own the documentation, semantic context, metadata, lineage, and retrieval patterns that AI systems depend on to understand and query Perplexity's data correctly. Own data modeling standards - define and champion dbt patterns, dimensional modeling practices, naming conventions, tests, and review processes. Lead data governance practices - define standards for access, ownership, lineage, documentation, retention, quality, and sensitive data handling across the analytical warehouse.

At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive. Interns work side-by-side with top engineers in the industry while having autonomy from the get-go. They contribute to user-facing products and are able to see their work go live quickly. Lyft fosters a collaborative environment in the office, so there's always a sharp mind eager to hear about your next idea. So what's yours? Responsibilities: Own your project, while checking in with other team members throughout the day with questions and updates You leave the code in a better state than when you found it (progressive refactor) You value reliability, ensured by testing Participate in code reviews to ensure code quality and distribute knowledge Continuous integration and deployment Go home knowing that your work today is meaningfully improving the lives of every Lyft driver and every Lyft passenger! Experience: Currently pursuing a Bachelor's or Master's degree in Computer Science, Data Science or related major from a university in Canada (required) , with a graduation date between December 2027 and Summer 2028 (required) . For any candidates who are master's students who worked between their bachelor's and master's programs: candidates should also have less than 2 years of relevant full-time work experience Available during Summer 2027 for an internship in Toronto Strong knowledge of CS fundamentals Knowledge of SQL and data modeling fundamentals Experience working with databases Excellent communication skills Interest in solving large scale data problems in a real world scenario Passion for community, sustainability, and/or transportation Benefits: Mental health benefits In addition to holidays, interns receive 2 days paid time off and 3 days sick time off Subsidized commuter benefits and Lyft ride credits Lyft is committed to creating an inclusive workforce that fosters belonging. Lyft belie

Apply early for priority consideration. Gallup has limited openings for this internship, and the position will be filled quickly. As a data engineering intern at Gallup, you’ll help build and modernize the data tools, infrastructure and pipelines that enable teams to work with data at scale. Alongside experienced technical leaders, you’ll put your Python and SQL skills to work on meaningful projects while learning best practices, experimenting with modern tools and seeing firsthand how strong data engineering supports better decisions. What You’ll Do During your internship, you will: Build foundational data tooling and infrastructure designed to scale as the volume and frequency of data grow Help modernize legacy data pipelines using Python, SQL and Gallup’s evolving data platform Troubleshoot data quality issues in large, imperfect datasets Apply data engineering practices to move, transform and validate data across systems Help build reliable, efficient data workflows Learn from senior technical leaders who will mentor you on engineering practices, tools and approaches Collaborate with engineers and interact directly with stakeholders to understand needs and deliver useful solutions Use AI-enabled development tools to improve the quality and efficiency of your work What You Need Enrollment in an associate, bachelor’s or graduate degree program in computer science, data engineering, information systems, statistics, math or a related field required Classroom or work experience writing SQL required, including joins, aggregations and troubleshooting query performance Classroom or work experience coding in Python for data processing, scripting or automation required Working knowledge of relational database and data modeling concepts, including schema design and dimensional or star-schema modeling, required Comfort using AI tools such as Claude, Claude Code or GitHub Copilot to support high-quality, efficient work required Understanding of ETL/ELT concepts and how data

Scale's Data Analytics team is centralized, embedding data analysts across delivery, operations, finance, and engineering to turn ambiguous problems into clear, actionable insights. As a Senior Data Analyst for Public Sector, you'll build analytics solutions from the ground up - partnering closely with cross-functional teams and leadership to solve complex problems, design dashboards, build tables, and perform ad-hoc analyses that shape how Scale runs and measures its Public Sector business. We're looking for someone who's relentlessly curious, comfortable owning ambiguity, and skilled at turning cross-functional relationships into sustainable, scalable analytics solutions. Active Secret U.S. security clearance required You will: Own data quality for your domain - document models, troubleshoot discrepancies, and establish centralized metric definitions Collaborate with cross-functional teams to identify, define, and track key metrics to be used in monitoring, planning, and one-off decision-making Develop repeatable, scalable analytical solutions, such as dashboards, data models, or standard processes Partner with Ops to identify and track efficiency initiatives, measuring their impact on cost, quality, and delivery outcomes over time Partner with Finance to analyze cost drivers across programs and help inform pricing strategy Be willing to deepdive when needed and dig into the messy details to get to the root of a problem Initiate and drive complex projects to completion with minimal guidance Ideally you'd have: 6+ years of relevant work experience Advanced SQL skills and experience building with data modeling tools (e.g. DBT) Experience building dashboards and reports with BI tools (e.g. Tableau, Metabase) Strong business acumen - comfortable partnering directly with cross-functional stakeholders Excellent communication skills - able to translate technical analysis into clear, actionable narratives Comfort with ambiguity in a fast-moving environment and able

We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Seattle, Washington D.C., Raleigh, London, and Amsterdam. Senior Data Scientist - Network Value (Credit) The Network Value Data Science team is helping Plaid build an industry-leading fintech consumer network with best-in-class products and user experiences. We are a product analytics team embedded in key product areas across Plaid. We support some of Plaid’s most important OKRs and help execute on product roadmaps. We translate ambiguous product questions into tractable analysis, serve as analytical thought partners throughout the org, identify opportunities to build better products, and champion a data-first decision-making approach everywhere we go. You’ll be a Data Scientist supporting Credit, a critical product area within Plaid’s Network Value portfolio. You’ll become a data and analytical thought partner to product managers, engineers, and cross-functional stakeholders, helping shape Credit product strategy, improve product performance and user experience, and grow Plaid’s consumer network. You’ll translate business questions into analytics projects, perform ad-hoc and strategic analysis, improve visibility into core systems through data modeling and dashboarding, create OKRs and KPIs tied to business goals and user experiences, a

Opportunity Overview We're hiring a Data Architect who will serve as a senior technical leader responsible for architecting the complete lifecycle data moves, transforms, and creates value across every product line we operate. You'll own the blueprint for our enterprise data lakehouse - setting the standards, governance, and strategy that elevate data from a byproduct of operations to the company's most valuable asset. We need someone who pairs deep mathematical and logical modeling expertise with a builder's instinct - someone who codes, prototypes fast, and proves out next-generation AI data architectures before committing the organization to them. This role would need experience with US healthcare domain data. What You'll Do Semantic Layer Governance: Set the technical standard and reference architecture for a universal semantic layer across multiple distinct data products, ensuring unified definitions of complex metrics and healthcare business entities across Architect the Enterprise Context Layer: Define, scale, and govern the global Enterprise Ontology (abstract domain maps, business entity classes, and entity-relationship rules) that serves as the backbone for Agentic AI systems. AI-Native Prototyping : Leverage modern AI-native development environments (such as Cursor, GitHub Copilot) to rapidly generate, iterate, and document blueprints for complex enterprise data schemas and metadata layers. Security & Compliance Leadership : Embed regulatory compliance (HIPAA, FHIR) and security principles into platform design. Partner with security and compliance teams to ensure architectural decisions support audit readiness and risk mitigation. Cross-Functional Collaboration : Partner with executive leadership, product management, and engineering to translate business requirements into scalable technical solutions. Communicate complex architectural concepts to both technical and non-technical stakeholders. ISMS roles and responsibilities Good knowledge of Informati

Senior Data Engineer, Public Sector As a Data Engineer for the Public Sector business unit, you will build Scale's analytical and business-intelligence infrastructure. Scale's customers process millions of tasks through our APIs, and we're looking for a talented Data Engineer to build scalable solutions to support this growth. You will have widespread purview, with responsibility for understanding, mining, aggregating, and exposing data across the entire business unit to support timely and efficient decision-making and data exploration. You will also implement Scale's data warehouse, data mart, and business intelligence reporting environments, and help users transition their workflows to these systems. This role requires collaboration with leadership and cross-functional teams to solve complex problems and develop sustainable, scalable data solutions. Your responsibilities will include both ad-hoc analyses and the creation of core data models and pipelines, directly impacting how Scale operates and evaluates its performance. You will: Work with operations, finance, and engineering to drive the development of pipelines that provide single-source-of-truth foundational accuracy Continually improve ongoing data pipelines and simplify self-service support for business stakeholders Perform regular system audits, and create data quality tests to ensure complete and accurate reporting of data/metrics Develop repeatable, scalable analytical solutions, such as data models, improved pipelines, or better underlying tables Have an active Secret security clearance (Top Secret preferred) Ideally You’d Have: 5+ years of relevant work experience in a role requiring application of data modeling and analytic skills Ability to create extensible and scalable data schema and pipelines that lay the foundation for downstream analysis Mastery of SQL and relational databases; experience with programming languages (e.g., Python/R) Experience building a reliable transformation layer and