Data Modelling Consultant Jobs

The Data Engineering team’s mission is to ensure high-quality data to enable data-informed decision-making across Asana. You will build data artifacts that are leveraged by Product and Business Data Science teams to optimize our user adoption, growth, and experience. In this role, you will partner with the Infrastructure team to build a self-service analytics platform for the company. This role is based in our Vancouver office with an office-centric hybrid schedule. The standard in-office days are Monday, Tuesday, and Thursday; most Asanas have the option to work from home on Wednesdays. Working from home on Fridays depends on the type of work you do and the teams with which you partner. If you're interviewing for this role, your recruiter will share more about the in-office requirements. What you’ll achieve Design, implement, and scale end-to-end data products that support growing data processing and analytical needs Transform raw data into actionable insights to drive product strategy and power in-depth analyses and reporting Leverage AI to build self-serve tools and accelerate Data/GTM workflows Partner with data scientists, domain experts, and engineering teams to develop a roadmap that aligns with our business goals Implement systems that guarantee data quality, governance, and availability About you 5+ years of experience in Data Engineering or Software Engineering Experience in data modeling and building scalable data pipelines involving complex transformations Proficiency in data processing and storage technologies like Databricks, AWS/S3, Python/Scala/Java, SQL, Spark, and Airflow Proactive and innovative in identifying and addressing performance bottlenecks in existing workflows Motivated to work closely with cross-functional partners to evolve our analytical data model Demonstrates curiosity about AI tools and emerging technologies, with a willingness to learn and leverage them to enhance productivity, collaboration, or decision-making At Asana, we're com

Who we are About Stripe Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world's largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone's reach while doing the most important work of your career. About the team The Data Transformation team is responsible for building and operating batch and real-time transformation pipelines and platforms for 400+ product teams at Stripe. We're aiming to make data development and management workflow at Stripe a breeze with emphasis on producing high-quality datasets used for critical analytics, dashboards, and workflows at Stripe. The products owned by this team are widely adopted with 800+ weekly active users improving developer productivity for data users manifold. The team is in an interesting phase of innovation tied to the topmost priority for Stripe, building and executing the strategic roadmap for streaming transformation, incremental processing, and ergonomic data modeling. We empower our users ranging from data scientists to engineers building pipelines to create exceptional Stripe product experiences while providing a robust transformation platform for critical dashboarding, analytics, and workflows at Stripe including user-facing reporting products Sigma and Radar. We adopt a combination of open-source technologies and in-house-built software to ensure high scalability, reliability, and usability of our transformation offerings. Key example technologies include Spark, Airflow, Iceberg, Hive, S3, SQL, Python, Scala, GRPC, Kafka, and FlinkSQL. What you'll do As an Engineering Manager of the Data Transformation team, you'll lead a team of engineers, collaborate with infrastructure and product engineering or

Role Purpose At Jumio, we’re building trusted identity solutions powered by machine learning and biometrics. As a Data Engineer II , you will take ownership of designing, building, and scaling data pipelines that power our ML systems. You’ll work closely with ML engineers, platform teams, and product stakeholders to enable robust dataset creation, benchmarking, and production-grade data workflows on AWS. What You’ll Do Design, build, and maintain scalable and reliable data pipelines for dataset creation, transformation, and benchmarking Own and optimize Airflow pipelines on AWS for data processing, orchestration, and evaluation workflows Write efficient, production-grade SQL and Python code for large-scale data processing and analysis Partner closely with ML engineers to enable model training, evaluation, and benchmarking pipelines Improve pipeline performance, reliability, and observability, ensuring high data quality in production Build and maintain systems to support model performance tracking and data drift monitoring Troubleshoot and resolve data issues across pipelines, ensuring minimal impact on ML workflows Contribute to data architecture decisions and best practices across the platform Collaborate cross-functionally with ML, platform, and data teams to support scalable ML infrastructure What We’re Looking For 3–5 years of experience in Data Engineering, Data Platforms, or related roles Strong proficiency in Python and SQL with experience in production systems Hands-on experience with AWS services (S3, EC2, SageMaker or similar) Solid experience building and managing Airflow (or similar orchestration tools) Strong understanding of data engineering fundamentals (ETL/ELT, data modeling, pipeline design) Experience working with large-scale datasets and distributed data systems Experience supporting ML workflows, datasets, or evaluation pipelines Strong problem-solving skills and ability to work independently in a fast-paced environment Nice to Have Experience w

Figma is growing our team of passionate creatives and builders on a mission to make design accessible to all. Figma’s platform helps teams bring ideas to life—whether you're brainstorming, creating a prototype, translating designs into code, or iterating with AI. From idea to product, Figma empowers teams to streamline workflows, move faster, and work together in real time from anywhere in the world. If you're excited to shape the future of design and collaboration, join us! We are looking for an experienced Data Scientist to join our growing data team. At Figma, Data Scientists are deeply embedded within cross-functional teams across the company—from Product to Finance, Marketing, and Platform. We are hiring across a number of roles in these areas. This is ideal for someone excited to own high-impact data projects, partner strategically with stakeholders, and shape the future of our products and business. Figma is uniquely positioned to harness advances in AI while keeping people at the heart of creative product development. As part of our Data Science team, you'll help address the most pressing challenges emerging from AI-driven development, shape next-generation data science workflows, and build AI-powered tools used by millions of product teams around the world. The ideal candidate will bring strong analytical and technical skills, business intuition, and a collaborative mindset to guide decision-making and unlock growth opportunities. You'll work on problems ranging from understanding user behavior to optimizing revenue strategies, improving internal tooling, and influencing product direction through experimentation and data modeling. This is a full-time role that can be held from one of our US hubs or remotely in the United States. What you'll do at Figma: Collaborate across teams to define and measure key metrics, design experiments, and uncover insights that inform strategic decisions Build models and analytical frameworks to support product, marketing, plat

Req: FEQ427R624 Location: Tokyo, Japan Language Requirements: Fluent Japanese required; Business-level English preferred As a Specialist Solutions Architect (SSA) - Data Warehousing, you will guide customers in their cloud data warehousing transformation with Databricks which span a large variety of use cases. You will be in a customer-facing role, working with and supporting Solution Architects, that requires hands-on production experience with large-scale data warehousing technologies and lakehouse architecture. SSAs help customers through evaluations and successful production planning for their business intelligence workloads while aligning their technical roadmap for the Databricks Data Intelligence Platform. As a deep go-to-expert reporting to the Specialist Field Engineering Manager, you will continue to strengthen your technical skills through mentorship, learning, and internal training programs and establish yourself in the data warehousing specialty - including performance tuning, data modeling, winning evaluations, architecture design, and production migration planning. The impact you will have: Provide technical leadership to guide strategic customers to successful cloud transformations on large-scale data warehousing workloads - ranging from evaluation to architecture design to production deployment Prove the value of the Databricks Intelligence Platform for customer workloads by architecting production workloads, including end-to-end pipeline load performance testing and optimization Become a technical expert in an area such as data warehousing evaluations or helping set up successful workload migrations Assist Solution Architects with more advanced aspects of the technical sale including custom proof of concept content, estimating workload sizing and performance, and tuning workloads for production Provide tutorials and training to improve community adoption (including hackathons and conference presentations) Contribute to the Databricks Communit

GitLab is the intelligent orchestration platform for DevSecOps. GitLab enables organizations to increase developer productivity, improve operational efficiency, reduce security and compliance risk, and accelerate digital transformation. More than 50 million registered users and more than 50% of the Fortune 100* trust GitLab to ship better, more secure software faster. The same principles built into our products are reflected in how our team works: we embrace AI as a core productivity multiplier, with all team members expected to incorporate AI into their daily workflows to drive efficiency, innovation, and impact. GitLab is where careers accelerate, innovation flourishes, and every voice is valued. Our high-performance culture is driven by our values and continuous knowledge exchange, enabling our team members to reach their full potential while collaborating with industry leaders to solve complex problems. Co-create the future with us as we build technology that transforms how the world develops software. * Fortune 500® is a registered trademark of Fortune Media IP Limited, used under license. Claim based on GitLab data. Fortune 100 refers to the top 20% ranked companies in the 2025 Fortune 500 list, published in June 2025. Fortune and Fortune Media IP Limited are not affiliated with, and do not endorse products or services of GitLab. An overview of this role As a Staff Data Analyst, you'll turn raw data into actionable insights that inform strategic decisions across GitLab's global Support Engineering organization. You'll design dashboards, reports, and data models that help leaders monitor performance, optimize costs, and improve customer outcomes. You'll also promote data innovation by experimenting thoughtfully and working through complex problems. What you'll do Own annual operating plan and capacity planning through data modeling and validation that support workforce and budget decisions. Build and maintain utilization and team-performance scorecards, in

What if the work you did every day could impact the lives of people you know? Or all of humanity? At Illumina, we are expanding access to genomic technology to realize health equity for billions of people around the world. Our efforts enable life-changing discoveries that are transforming human health through the early detection and diagnosis of diseases and new treatment options for patients. Working at Illumina means being part of something bigger than yourself. Every person, in every role, has the opportunity to make a difference. Surrounded by extraordinary people, inspiring leaders, and world changing projects, you will do more and become more than you ever thought possible. Summary The Staff Data Engineer is a seasoned, hands-on engineer who designs, builds, and scales data products on our cloud lakehouse, powering analytics, reporting, and AI/ML across Illumina. We are looking for someone with strong proficiency in Python, SQL, and data modeling, a solid understanding of distributed systems and system design who has built and scaled data products on modern cloud platforms such as Databricks and Snowflake. This is a hands-on, senior individual-contributor role with end-to-end ownership and leadership spanning multiple domains such as Supply Chain, Manufacturing and Quality, including mentoring engineers on our global (India-based) team. Responsibilities Partner across business, AI, and platform teams translating domain needs (e.g., SAP, Manufacturing, Quality) into well-modeled, governed and scalable data products. Design, build, and scale end-to-end data products on Databricks (and interoperating with Snowflake) — from ingestion through curated, analytics-ready datasets following a medallion (Bronze/Silver/Gold) architecture. Develop reusable frameworks, libraries, and

FEQ427R92 As a Specialist Solutions Architect (SSA) - Data Warehousing, you will guide customers in their cloud data warehousing transformation with Databricks which span a large variety of use cases. You will be in a customer-facing role, working with and supporting Solution Architects, that requires hands-on production experience with large-scale data warehousing technologies and lakehouse architecture. SSAs help customers through evaluations and successful production planning for their business intelligence workloads while aligning their technical roadmap for the Databricks Data Intelligence Platform. As a deep go-to-expert reporting to the Specialist Field Engineering Manager, you will continue to strengthen your technical skills through mentorship, learning, and internal training programs and establish yourself in the data warehousing specialty - including performance tuning, data modeling, winning evaluations, architecture design, and production migration planning. The impact you will have: Provide technical leadership to guide strategic customers to successful cloud transformations on large-scale data warehousing workloads - ranging from evaluation to architecture design to production deployment Prove the value of the Databricks Intelligence Platform for customer workloads by architecting production workloads, including end-to-end pipeline load performance testing and optimization Become a technical expert in an area such as data warehousing evaluations or helping set up successful workload migrations Assist Solution Architects with more advanced aspects of the technical sale including custom proof of concept content, estimating workload sizing and performance, and tuning workloads for production Provide tutorials and training to improve community adoption (including hackathons and conference presentations) Contribute to the Databricks Community What we look for: 5+ years experience in a technical role with expertise in data warehousing - such as query tuning

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

The Platform team creates the technology that enables Spotify to learn quickly and scale easily, enabling rapid growth in our users and our business around the globe. Spanning many disciplines, we work to make the business work; creating the infrastructure, tooling, frameworks, and capabilities needed to welcome a billion customers. The Data Platform team develops and maintains the infrastructure that supports Spotify’s data ecosystem. Within Data Platform, the Data Collection product area builds infrastructure that makes it easier to collect and consume data at scale. You’ll join a team focused on making user behaviour data seamless and reliable for data teams across Spotify. What You'll Do Collaborate with product and engineering teams to build and support critical data infrastructure and datasets. Build systems that collect and process user behaviour data at scale. Help power Spotify’s personalization and recommendation systems, as well as the tools teams use to build and test new features. Design and evolve data architectures that support high data volumes and a broad range of internal users. Improve the reliability, scalability, and usability of our data infrastructure. Work across disciplines to solve complex data challenges and make an impact across Spotify. Who You Are You are an experienced Data Engineer, and proficient in backend development using Java. You are comfortable working with large-scale datasets using SQL and platforms such as BigQuery. You have experience with JVM-based data processing frameworks such as Flink, Beam, Dataflow, or Spark. You are familiar with DevOps practices, cloud infrastructure, containerized applications, and Kubernetes fundamentals. You have experience with data modeling and schema design. You care about high-quality code and engineering practices such as continuous delivery and automated testing.</

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 (Plaid App) 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 Senior Data Scientist supporting Plaid App, a critical user-facing product within Plaid’s Network Value portfolio. In this role, you’ll become a data and analytical thought partner to product managers and engineers, helping drive data-informed product development as the team launches and scales the product from 0 to 1. 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 metrics tied to business goals, and support feature ship decisions

Shape the Future with Dun & Bradstreet At Dun & Bradstreet, we believe data has the power to create a better tomorrow. As a global leader in business decisioning data and analytics, we help companies worldwide grow, manage risk, and innovate. For over 180 years, businesses have trusted us to turn uncertainty into opportunity. We’re a diverse, global team that values creativity, collaboration, and bold ideas. Are you ready to make an impact and help shape what’s next? Join us! Explore opportunities at dnb.com/careers. Sr. Data Cloud Engineer(s)(multiple positions) – Duties are using relational database systems, CI/CD pipeline, Kubernetes for application deployments, terraform IAC, Observability using Splunk, server-side GitHub REST API, & Snowflake, Spark, & Python to design, automate, implement & maintain data architecture patterns in AWS & GCP for data collection infrastructure & pipelines for multi-region cloud-based big data IaaS & PaaS solutions including performing requirements & document use technology analysis; coordinating data integration & ETL processes; supporting data security, integrity & cost controls; automating data processes utilizing Cloud Composer & develop SQL scripts for data modeling; performing end-to-end pipeline load performance testing; supporting technology upgrades through PostgreSQL & Cloud SQL cloud migrations; performing SQL & NoSQL database tuning & maintenance; providing guidance on data management & SQL optimization; & developing data warehousing management policies. Requires Bach degree in Comp Science, Comp Engineering, Information Technology (IT) or related field & 5 yrs exp in job duties as stated. Position is with Dun & Bradstreet in Jacksonville, FL. Inquire and send resume through Dun & Bradstreet’s job board at https://jobs.lever.co/dnb. Position is under Sr. Data Cloud Engineer for Jacksonville, FL. #LI-DNI

About the Role We are seeking an experienced Senior Data Engineer to join our growing Data & Analytics team. This role will be responsible for designing, developing and maintaining scalable data pipelines and infrastructure to support our organization’s data needs. Specific Responsibilities: Design, develop, and maintain robust data pipelines and infrastructure to support data integration, processing, and storage Collaborate with analysts and other stakeholders to understand data requirements and translate them into technical solutions Implement best practices for data governance, security, and quality assurance to ensure the reliability and accuracy of data Explore and evaluate new technologies and tools to enhance our data infrastructure and capabilities Design and implement complex data models on Azure Synapse Analytics to support data warehousing Utilize Azure DevOps for continuous integration, continuous delivery, and agile project management. Manage and optimize Apache Spark pools for high-performance data processing tasks. Develop and maintain ETL processes and data pipelines using Python scripting. Ensure data quality and integrity across all platforms and systems. Collaborate with cross-functional teams to translate business needs into technical specifications. Stay current with industry trends and advancements in Azure data services. What you'll need: Bachelor’s degree in Computer Science, Engineering, or related field Minimum of 5+ years experience in data engineering roles, with a strong focus on building and maintaining data pipelines and infrastructure Strong proficiency in Azure Synapse Analytics, Azure DevOps, and Apache Spark pools. Expertise in Python scripting and other programming languages such as SQL and Java Senior level knowledge of data modeling, processes, and data warehouse concepts Strong communication skills and ability to effectively communicate technical concepts to non-technical stakeholder

At Experient, we provide experience, technology, and transformation support to our clients, creating a dynamic work environment. We value collaboration, prioritize effective communication, and focus on delivering results quickly and practically. Whether you’re looking to deepen your expertise or take on new challenges, we’re here to support your journey every step of the way. We are seeking a Senior Snowflake Data Engineer to join our development team. This individual will be responsible for hands-on building, enhancing, and supporting multiple high-volume data pipelines used by our client to process business critical order transaction data. Location: Dallas, Texas - Hybrid What you’ll do: Responsible for hands on database work and execution of the tactical work Involved in thought leadership Define future state architecture Set tech design principles Align the team on technical direction Pipeline Development: Design, implement, and maintain ELT/ETL pipelines using Snowflake, dbt, and orchestration tools Data Modeling: Create and optimize star/snowflake schemas, ensuring data integrity and scalability SQL & Performance Tuning: Write complex SQL queries, optimize queries for performance, and troubleshoot issues Integration: Connect Snowflake with cloud platforms (AWS, Azure, GCP) and third-party tools (e.g., Alation, Power BI) What you will bring: 8-10 years of experience in data engineering with Snowflake Extensive Snowflake experience Snowflake certifications required Must have hands on experience and execute the tactical work Experienced in thought leadership, defining future state architecture, setting tech design principles, aligning the team on technical direction. Familiarity with Snowflake Cortex (their AI tooling) Extensive DBT experience Python and Apache Airflow are preferred Advanced SQL and Snowflake SQL ETL with dbt and orchestration Cloud platform proficiency Knowledge of big data technologies (Hadoop, Spark, Kafka)

Zoox is seeking a high-agency, hands-on Lead Analytics Engineer to bridge the gap between our complex enterprise data sources and our AI-driven decision-making systems. You will lead the design and implementation of our semantic layer and data modeling strategy, ensuring that data from SAP (S/4HANA, Ariba, BRIM, ME), Workday, Salesforce, and Anaplan is transformed into clean, performant, and "AI-ready" datasets. This is a critical leadership role for a builder who wants to own the data foundation that powers our intelligent agents and business-wide analytical workflows. In this role, you will: Design and maintain a unified semantic model that provides a "single source of truth" for cross-functional stakeholders, AI Agents, Self Serve Analytics and Executive dashboards. Collaborate with Data & AI Engineers to structure and optimize the data that allows to query enterprise knowledge with high accuracy and low latency. Establish organizational standards for data modeling, version control, testing, and documentation to ensure high data quality and system maintainability. Implement automated testing and observability frameworks that proactively identify data anomalies and "self-heal" pipelines, ensuring our data is always reliable for downstream consumption. Partner with cross-functional business leaders to translate complex operational requirements into high-impact, scalable data solutions. Qualifications: 10+ years in Data Engineering & Analytics, with extensive hands-on experience building a semantic framework using Python, SQL and modern orchestration frameworks (e.g. Airflow, Lakeflow, Argo). With at least 2+ years of hands-on experience deploying AI generated code. Extensive experience using modern data stacks (e.g. Snowflake/Databricks, Big Query) to build complex, enterprise-grade data models. Deep understanding of data structures within large-scale enterprise platf