Data Modelling Consultant Jobs

We are looking for a hands-on VP, Product Analytics to lead Product Analytics and build an AI-native analytics operating system across the company’s crypto, stocks, prediction markets, perpetuals, and stock futures products. You will set the analytics strategy, lead and develop the Product Analytics team, and ensure data consistently shapes product and business decisions. This is a player-coach role: you will operate at executive level while remaining technically close enough to review PRs, guide data modeling and pipeline design, and challenge analytical conclusions. You will own product and business reporting, experimentation, release measurement, diagnostic analysis, the Amplitude data stack, and the systems through which analytics work is delivered at scale. Lead Product Analytics Set the vision, priorities, operating model, and quality standards for Product Analytics. Hire, coach, and develop a high-performing team. Review analytical and data-engineering PRs, providing guidance on SQL, data models, pipelines, metric definitions, and methodology. Represent Product Analytics in executive and product decision-making. Allocate team capacity toward the company’s highest-impact opportunities. Build an AI-native analytics operating system Design how analytics work moves from business questions to trusted decisions across intake, data discovery, analysis, validation, reporting, and knowledge management. Build reusable AI tools to automate repetitive workflows, encode analytical standards, and improve the speed, quality, and consistency of delivery. Establish appropriate governance, validation, and human review for high-stakes decisions. Measure the system’s impact on turnaround time, analytical quality, experimentation throughput

We are looking for a hands-on VP, Product Analytics to lead Product Analytics and build an AI-native analytics operating system across the company’s crypto, stocks, prediction markets, perpetuals, and stock futures products. You will set the analytics strategy, lead and develop the Product Analytics team, and ensure data consistently shapes product and business decisions. This is a player-coach role: you will operate at executive level while remaining technically close enough to review PRs, guide data modeling and pipeline design, and challenge analytical conclusions. You will own product and business reporting, experimentation, release measurement, diagnostic analysis, the Amplitude data stack, and the systems through which analytics work is delivered at scale. Lead Product Analytics Set the vision, priorities, operating model, and quality standards for Product Analytics. Hire, coach, and develop a high-performing team. Review analytical and data-engineering PRs, providing guidance on SQL, data models, pipelines, metric definitions, and methodology. Represent Product Analytics in executive and product decision-making. Allocate team capacity toward the company’s highest-impact opportunities. Build an AI-native analytics operating system Design how analytics work moves from business questions to trusted decisions across intake, data discovery, analysis, validation, reporting, and knowledge management. Build reusable AI tools to automate repetitive workflows, encode analytical standards, and improve the speed, quality, and consistency of delivery. Establish appropriate governance, validation, and human review for high-stakes decisions. Measure the system’s impact on turnaround time, analytical quality, experimentation throughput

We are looking for a hands-on VP, Product Analytics to lead Product Analytics and build an AI-native analytics operating system across the company’s crypto, stocks, prediction markets, perpetuals, and stock futures products. You will set the analytics strategy, lead and develop the Product Analytics team, and ensure data consistently shapes product and business decisions. This is a player-coach role: you will operate at executive level while remaining technically close enough to review PRs, guide data modeling and pipeline design, and challenge analytical conclusions. You will own product and business reporting, experimentation, release measurement, diagnostic analysis, the Amplitude data stack, and the systems through which analytics work is delivered at scale. Lead Product Analytics Set the vision, priorities, operating model, and quality standards for Product Analytics. Hire, coach, and develop a high-performing team. Review analytical and data-engineering PRs, providing guidance on SQL, data models, pipelines, metric definitions, and methodology. Represent Product Analytics in executive and product decision-making. Allocate team capacity toward the company’s highest-impact opportunities. Build an AI-native analytics operating system Design how analytics work moves from business questions to trusted decisions across intake, data discovery, analysis, validation, reporting, and knowledge management. Build reusable AI tools to automate repetitive workflows, encode analytical standards, and improve the speed, quality, and consistency of delivery. Establish appropriate governance, validation, and human review for high-stakes decisions. Measure the system’s impact on turnaround time, analytical quality, experimentation throughput

Job Overview We are looking for an Analytics Engineer who will own dbt Cloud in our organisation and maintain the analytics pipelines built on top of it. You will administer dbt Cloud across multiple independent projects, keep our business-critical pipelines running (including finance data models), and support analysts in other teams as a mentor and go-to person for dbt. Our stack is Snowflake, dbt and AWS, with Python for tooling and automation. We are also building AI-assisted and agentic workflows into our daily work, and you will take an active part in this. Reports to Director of Mobile and New Games What will you do? Administer dbt Cloud for the whole organisation: project setup, environments, permissions, CI/CD, version upgrades and cost control Set up and support multiple independent dbt projects, define common standards and conventions so teams stay aligned Develop and maintain production dbt pipelines, including complex finance models where accuracy and trust are critical Mentor analysts on dbt, SQL modeling, testing and code review Tune Snowflake performance and cost, improve pipeline reliability and monitoring Build tooling and automation in Python (AWS Lambda, S3, orchestration, metadata workflows) Drive AI adoption in our analytics engineering work: build common AI skills for dbt development (documentation, testing, code generation) and automated agentic workflows that the whole team can reuse Follow current trends in AI tooling and agentic systems, evaluate new approaches and bring the useful ones into the team What are we looking for? 4+ years of experience in analytics engineering, data engineering or a similar role Strong hands-on dbt experience, ideally including dbt Cloud administration (projects, environments, jobs, CI/CD), not only model development Strong SQL and data modeling skills Production experience with Snowflake, including performance tuning and cost awareness Python for data tooling and automation Experience with AWS services used in

Job Overview We are looking for an Analytics Engineer who will own dbt Cloud in our organisation and maintain the analytics pipelines built on top of it. You will administer dbt Cloud across multiple independent projects, keep our business-critical pipelines running (including finance data models), and support analysts in other teams as a mentor and go-to person for dbt. Our stack is Snowflake, dbt and AWS, with Python for tooling and automation. We are also building AI-assisted and agentic workflows into our daily work, and you will take an active part in this. Reports to Director of Mobile and New Games What will you do? Administer dbt Cloud for the whole organisation: project setup, environments, permissions, CI/CD, version upgrades and cost control Set up and support multiple independent dbt projects, define common standards and conventions so teams stay aligned Develop and maintain production dbt pipelines, including complex finance models where accuracy and trust are critical Mentor analysts on dbt, SQL modeling, testing and code review Tune Snowflake performance and cost, improve pipeline reliability and monitoring Build tooling and automation in Python (AWS Lambda, S3, orchestration, metadata workflows) Drive AI adoption in our analytics engineering work: build common AI skills for dbt development (documentation, testing, code generation) and automated agentic workflows that the whole team can reuse Follow current trends in AI tooling and agentic systems, evaluate new approaches and bring the useful ones into the team What are we looking for? 4+ years of experience in analytics engineering, data engineering or a similar role Strong hands-on dbt experience, ideally including dbt Cloud administration (projects, environments, jobs, CI/CD), not only model development Strong SQL and data modeling skills Production experience with Snowflake, including performance tuning and cost awareness Python for data tooling and automation Experience with AWS services used in

Job Overview We are looking for an Analytics Engineer who will own dbt Cloud in our organisation and maintain the analytics pipelines built on top of it. You will administer dbt Cloud across multiple independent projects, keep our business-critical pipelines running (including finance data models), and support analysts in other teams as a mentor and go-to person for dbt. Our stack is Snowflake, dbt and AWS, with Python for tooling and automation. We are also building AI-assisted and agentic workflows into our daily work, and you will take an active part in this. Reports to Director of Mobile and New Games What will you do? Administer dbt Cloud for the whole organisation: project setup, environments, permissions, CI/CD, version upgrades and cost control Set up and support multiple independent dbt projects, define common standards and conventions so teams stay aligned Develop and maintain production dbt pipelines, including complex finance models where accuracy and trust are critical Mentor analysts on dbt, SQL modeling, testing and code review Tune Snowflake performance and cost, improve pipeline reliability and monitoring Build tooling and automation in Python (AWS Lambda, S3, orchestration, metadata workflows) Drive AI adoption in our analytics engineering work: build common AI skills for dbt development (documentation, testing, code generation) and automated agentic workflows that the whole team can reuse Follow current trends in AI tooling and agentic systems, evaluate new approaches and bring the useful ones into the team What are we looking for? 4+ years of experience in analytics engineering, data engineering or a similar role Strong hands-on dbt experience, ideally including dbt Cloud administration (projects, environments, jobs, CI/CD), not only model development Strong SQL and data modeling skills Production experience with Snowflake, including performance tuning and cost awareness Python for data tooling and automation Experience with AWS services used in

Job Overview We are looking for an Analytics Engineer who will own dbt Cloud in our organisation and maintain the analytics pipelines built on top of it. You will administer dbt Cloud across multiple independent projects, keep our business-critical pipelines running (including finance data models), and support analysts in other teams as a mentor and go-to person for dbt. Our stack is Snowflake, dbt and AWS, with Python for tooling and automation. We are also building AI-assisted and agentic workflows into our daily work, and you will take an active part in this. Reports to Director of Mobile and New Games What will you do? Administer dbt Cloud for the whole organisation: project setup, environments, permissions, CI/CD, version upgrades and cost control Set up and support multiple independent dbt projects, define common standards and conventions so teams stay aligned Develop and maintain production dbt pipelines, including complex finance models where accuracy and trust are critical Mentor analysts on dbt, SQL modeling, testing and code review Tune Snowflake performance and cost, improve pipeline reliability and monitoring Build tooling and automation in Python (AWS Lambda, S3, orchestration, metadata workflows) Drive AI adoption in our analytics engineering work: build common AI skills for dbt development (documentation, testing, code generation) and automated agentic workflows that the whole team can reuse Follow current trends in AI tooling and agentic systems, evaluate new approaches and bring the useful ones into the team What are we looking for? 4+ years of experience in analytics engineering, data engineering or a similar role Strong hands-on dbt experience, ideally including dbt Cloud administration (projects, environments, jobs, CI/CD), not only model development Strong SQL and data modeling skills Production experience with Snowflake, including performance tuning and cost awareness Python for data tooling and automation Experience with AWS services used in

Elastic, the Search AI Company, enables everyone to find the answers they need in real time, using all their data, at scale — unleashing the potential of businesses and people. The Elastic Search AI Platform, used by more than 50% of the Fortune 500, brings together the precision of search and the intelligence of AI to enable everyone to accelerate the results that matter. By taking advantage of all structured and unstructured data — securing and protecting private information more effectively — Elastic’s complete, cloud-based solutions for search, security, and observability help organizations deliver on the promise of AI. What is The Role This role is responsible for leading the migration from Salesforce CPQ to Revenue Cloud and being responsible for the end-to-end Quote-to-Cash capability. The manager will ensure that quoting, pricing, contracting, ordering, and billing processes operate optimally and scale with business growth. The role requires close partnership with Sales, Finance, Legal, and IT. What You Will Be Doing Experience in Salesforce CPQ and Revenue Cloud architecture. Proven experience in end-to-end Quote-to-Cash implementations (CPQ, Billing, Subscription Management, Revenue Recognition). Experience with the Salesforce platform (Sales Cloud, Service Cloud, Lightning, Apex, Visualforce, Flows). Expertise in data modeling, system integrations (MuleSoft, APIs, middleware, ERP), and security design. Deep understanding of pricing strategies, product modeling, discounting, approval workflows, subscription lifecycle management, and revenue forecasting. Own end-to-end engineering delivery for the assigned Q2C workstream. Ensure all requirements, dependencies, and user stories are complete before development begins. Translate business and solution requirements into executable engineering work. Manage team capacity and sprint commitments in alignment with Scrum best practices. Proactively identify delivery risks and implement mitigation plans before timeline

Elastic, the Search AI Company, enables everyone to find the answers they need in real time, using all their data, at scale — unleashing the potential of businesses and people. The Elastic Search AI Platform, used by more than 50% of the Fortune 500, brings together the precision of search and the intelligence of AI to enable everyone to accelerate the results that matter. By taking advantage of all structured and unstructured data — securing and protecting private information more effectively — Elastic’s complete, cloud-based solutions for search, security, and observability help organizations deliver on the promise of AI. What is The Role The Prism team builds the systems that turn Elastic Security's raw data into a trustworthy picture of the users, hosts, and services in a customer's environment. We power the Entity Store, entity risk scoring, entity resolution, and the data maintainers that analysts rely on to understand who did what, what's risky, and what to investigate next. We build features that have to work at scale, and we care deeply about correctness and performance. As a senior engineer, you will lead technical work across this area. You'll partner with product, design, and engineering to turn hard, ambiguous problems into reliable, performant systems that ship to real security teams. We're a distributed team — we work across time zones and collaborate in GitHub, Slack, design reviews, and code reviews. What You Will Be Doing Own small-to-medium projects from technical design through production, including data modeling, background tasks, and APIs. Make our features fast and reliable at scale: profile and optimize queries, tame timeouts, and design for a range of customer topologies. Write high-quality TypeScript and React code, with Node.js services and APIs where needed. Build telemetry, tests, and safeguards so the systems behave predictably in production. Review code, support teammates, and improve team practices. Partner with teams ac

Elastic, the Search AI Company, enables everyone to find the answers they need in real time, using all their data, at scale — unleashing the potential of businesses and people. The Elastic Search AI Platform, used by more than 50% of the Fortune 500, brings together the precision of search and the intelligence of AI to enable everyone to accelerate the results that matter. By taking advantage of all structured and unstructured data — securing and protecting private information more effectively — Elastic’s complete, cloud-based solutions for search, security, and observability help organizations deliver on the promise of AI. What is The Role The Prism team builds the systems that turn Elastic Security's raw data into a trustworthy picture of the users, hosts, and services in a customer's environment. We power the Entity Store, entity risk scoring, entity resolution, and the data maintainers that analysts rely on to understand who did what, what's risky, and what to investigate next. We build features that have to work at scale, and we care deeply about correctness and performance. As a senior engineer, you will lead technical work across this area. You'll partner with product, design, and engineering to turn hard, ambiguous problems into reliable, performant systems that ship to real security teams. We're a distributed team — we work across time zones and collaborate in GitHub, Slack, design reviews, and code reviews. What You Will Be Doing Own small-to-medium projects from technical design through production, including data modeling, background tasks, and APIs. Make our features fast and reliable at scale: profile and optimize queries, tame timeouts, and design for a range of customer topologies. Write high-quality TypeScript and React code, with Node.js services and APIs where needed. Build telemetry, tests, and safeguards so the systems behave predictably in production. Review code, support teammates, and improve team practices. Partner with teams ac

Elastic, the Search AI Company, enables everyone to find the answers they need in real time, using all their data, at scale — unleashing the potential of businesses and people. The Elastic Search AI Platform, used by more than 50% of the Fortune 500, brings together the precision of search and the intelligence of AI to enable everyone to accelerate the results that matter. By taking advantage of all structured and unstructured data — securing and protecting private information more effectively — Elastic’s complete, cloud-based solutions for search, security, and observability help organizations deliver on the promise of AI. What is The Role The Prism team builds the systems that turn Elastic Security's raw data into a trustworthy picture of the users, hosts, and services in a customer's environment. We power the Entity Store, entity risk scoring, entity resolution, and the data maintainers that analysts rely on to understand who did what, what's risky, and what to investigate next. We build features that have to work at scale, and we care deeply about correctness and performance. As a senior engineer, you will lead technical work across this area. You'll partner with product, design, and engineering to turn hard, ambiguous problems into reliable, performant systems that ship to real security teams. We're a distributed team — we work across time zones and collaborate in GitHub, Slack, design reviews, and code reviews. What You Will Be Doing Own small-to-medium projects from technical design through production, including data modeling, background tasks, and APIs. Make our features fast and reliable at scale: profile and optimize queries, tame timeouts, and design for a range of customer topologies. Write high-quality TypeScript and React code, with Node.js services and APIs where needed. Build telemetry, tests, and safeguards so the systems behave predictably in production. Review code, support teammates, and improve team practices. Partner with teams ac

Elastic, the Search AI Company, enables everyone to find the answers they need in real time, using all their data, at scale — unleashing the potential of businesses and people. The Elastic Search AI Platform, used by more than 50% of the Fortune 500, brings together the precision of search and the intelligence of AI to enable everyone to accelerate the results that matter. By taking advantage of all structured and unstructured data — securing and protecting private information more effectively — Elastic’s complete, cloud-based solutions for search, security, and observability help organizations deliver on the promise of AI. What is The Role The Prism team builds the systems that turn Elastic Security's raw data into a trustworthy picture of the users, hosts, and services in a customer's environment. We power the Entity Store, entity risk scoring, entity resolution, and the data maintainers that analysts rely on to understand who did what, what's risky, and what to investigate next. We build features that have to work at scale, and we care deeply about correctness and performance. As a senior engineer, you will lead technical work across this area. You'll partner with product, design, and engineering to turn hard, ambiguous problems into reliable, performant systems that ship to real security teams. We're a distributed team — we work across time zones and collaborate in GitHub, Slack, design reviews, and code reviews. What You Will Be Doing Own small-to-medium projects from technical design through production, including data modeling, background tasks, and APIs. Make our features fast and reliable at scale: profile and optimize queries, tame timeouts, and design for a range of customer topologies. Write high-quality TypeScript and React code, with Node.js services and APIs where needed. Build telemetry, tests, and safeguards so the systems behave predictably in production. Review code, support teammates, and improve team practices. Partner with teams ac

The Manager, Business Intelligence leads a team of product owners and reporting engineers who build and maintain the reporting our corporate business areas and venues run on. You set the standard for how data is modeled, defined, and delivered in Amazon Quick Suite (Quick Sight) on top of Redshift and Snowflake, and you connect two halves of the team: the product owners who own the relationship with the business and the backlog, and the engineers who build against it. Success is measured less by dashboards shipped and more by decisions improved — how fast a venue GM or a corporate leader gets a reliable answer, and how much they trust it. Team leadership Lead, coach, and develop a team of BI product owners and reporting engineers; own hiring, onboarding, performance, and career growth across two distinct skill tracks. Develop product owners in requirements gathering, backlog management, stakeholder communication, acceptance criteria, and saying no well. Develop reporting engineers in SQL, data modeling, Quick Suite development, and engineering craft. Hold the line between the two: ensure requirements arrive well-defined and that engineers aren't absorbing unscoped work mid-sprint. Run the team's delivery cadence — intake, prioritization, sprint or release planning, and demo/review — balancing executive asks against roadmap commitments and technical debt. Establish and enforce team standards for SQL, data modeling, version control, code review, testing, documentation, and dashboard design consistency. Product management of the reporting portfolio Own the BI product roadmap across corporate functions and venue operations; make the trade-off calls when demand exceeds capacity. Ensure each business area — Finance, Operations, Marketing, F&B, Guest Experience — has a clear product owner relationship and a visible backlog.<

Our vision is to transform how the world uses information to enrich life for all . Micron Technology is a world leader in innovating memory and storage solutions that accelerate the transformation of information into intelligence, inspiring the world to learn, communicate and advance faster than ever. Fab16s (Tainan) Scanner Process Manager As a Fab16s (Tainan) Process Manager, you will… Lead and manage a team of process engineers to oversee process development, optimization, and sustaining in semiconductor manufacturing. Drive process performance, yield improvement, and cost optimization while leveraging AI-driven analytics, automation, and advanced data modeling to enhance decision-making, process control, and operational efficiency. Provide technical leadership and support across manufacturing, equipment, and integration teams. Responsibilities and Tasks • Forecast and manage process-related operational costs, including materials, yield loss, and metrology expenses. Drive cost reduction initiatives through AI-based yield analysis and predictive modeling. • Develop and implement robust process control systems to ensure stability, repeatability, and high yield. Leverage AI-assisted fault detection, root cause analysis (RCA), and knowledge systems to improve excursion response and reduce variability. • Work closely with Equipment Engineering and Manufacturing teams to address process constraints and optimize OEE, yield, and throughput. Utilize AI/ML models to improve process windows and sampling strategies. • Partner with cross-functional teams to develop scalable process solutions, integrating AI-enabled monitoring systems, digital dashboards, and smart alerting for real-time process health tracking. • Provide support to ensure compliance with EHSS

Our vision is to transform how the world uses information to enrich life for all . Micron Technology is a world leader in innovating memory and storage solutions that accelerate the transformation of information into intelligence, inspiring the world to learn, communicate and advance faster than ever. Department Intro... We are part of Micron’s Technology Development Operations team, focused on driving operational excellence and data integrity across advanced semiconductor fabrication environments. Our team bridges engineering processes with modern data architectures to improve production efficiency, cycle times, and operational performance. A few sentences about the department here. Position Overview... In this role, you will apply industrial engineering principles, data modeling, and data cleansing to optimize manufacturing workflows and systems. You will collaborate with process teams and data infrastructure owners to collect, warehouse, and visualize key production metrics that support high-volume manufacturing decisions. Responsibilities: Design, implement, and maintain data collection pipelines and warehousing workflows across manufacturing systems. Develop automated data cleansing procedures and data validation frameworks to ensure high fidelity in operational metrics. Build clear, intuitive data visualizations and reporting dashboards to communicate equipment, cycle time, and production trends. Apply industrial and manufacturing engineering methodologies to analyze bottlenecks and optimize floor operations. Utilize SQL, Python, and related tools to perform root-cause analyses and model complex operational problems. Utilize AI tools to analyze data, identify improvement opportunities, automate workflows, and improve operational efficiency. Partner cross-functionally