Analytics Engineer

• Design and build our data models and transformations, applying software engineering best practices in dbt on top of our Google BigQuery warehouse to produce datasets that are accurate, performant, and easy to reason about.
• Own the semantic layer in Lightdash, defining metrics, dimensions, and governed datasets so the business gets one consistent, trusted answer to every question — no matter who (or what) is asking.
• Act as a leader for our data modeling standards, shaping conventions, review practices, and documentation that keep the transformation layer maintainable as it grows.
• Develop end-to-end with AI coding agents. Our teams build with Claude Code and similar tools, and you'll use AI-assisted workflows to move faster while upholding quality through testing and review.
• Enable AI-driven analytics, treating the semantic and governance layers as the foundation that agents and MCP connections build on to deliver trustworthy, self-serve answers from data.
• Advance metric governance and definitional consistency, contributing to the auditability and reporting rigor expected of a company operating at scale and under growing scrutiny.
• Partner with cross-functional stakeholders to gather requirements, identify high-value reporting and dashboarding use cases, and ship the models and metrics that drive decisions.
• Train and enable users on self-serve analytics capabilities and best practices, growing the number of people across the business who can confidently answer their own questions with data.

Senior Analytics Engineer

• Build and maintain interactive dashboards and standardized reports in the enterprise BI platform (e.g., Power BI, Tableau) that address recurring commercial reporting needs.
• Translate business user requirements into clear, accurate, and usable visualizations aligned to approved data models and metric definitions.
• Apply consistent design standards, layout conventions, and UX best practices to ensure reports are intuitive and accessible to non-technical users.
• Perform unit testing and validation of report outputs against source data to ensure accuracy before deployment.
• Build and maintain semantic layer objects (e.g., measures, dimensions, hierarchies, calculated fields) within the governed BI and analytics platform.
• Ensure semantic objects conform to approved business taxonomy, metric definitions, and naming conventions.
• Gather and clarify requirements from commercial business users to ensure dashboard and reporting solutions meet their analytical needs.
• Provide first-line support for BI platform questions, report issues, and data interpretation queries from business users.
• Monitor dashboard and report data freshness, flagging anomalies or stale data to the appropriate pipeline owners.
• Support the development and testing of agentic AI workflows that leverage governed semantic layer assets and BI data products for automated commercial insights delivery.

Commercial Analytics Engineer – Business Decision Intelligence

• Experiment with and combine data from a variety of data sources and with increasing complexity for use in analytics & reporting solutions.
• Own and maintain the MyInsights reporting portal, ensuring analytics and reporting solutions are organized, accessible, and aligned with customer needs and business priorities.
• Supports user training, acts as an ambassador of the solution, and partners with project teams to ensure smooth and launch and adoption
• Elicit and clarify business needs in order to design and develop appropriate analytics & reporting solutions that satisfy those needs
• Update and maintain analytics & reporting solutions according to industry standard methodologies
• Find opportunities to consolidate analytics & reporting solutions
• Establish and monitor adoption criteria based on utilization statistics
• Maintain relationships with customer groups and respond to business needs as applicable under the guidance of more senior associates
• Technical design, build and iteration of analytics & reporting solutions.
• Intakes feedback on prototypes and iterates continuously to improve user experience and customer engagement.
• Partners with IT and other tech vendors and partners for necessary technical development and support as required.
• Proposes mock and prototype low-fidelity designs using design thinking methodology.
• Understands how the solutions in the team work together and finds opportunities to drive consistent service experience across the suite of solutions.
• Work with customer teams to ensure proper insights are extracted from solutions
• Work in close partnership with more senior Associates, who will provide guidance and technical direction to build the Analyst’s skills.
• Develop more complex solutions, seeking the expertise of more senior associates as needed.

Senior Engineer, Analytics & Productivity Solutions

• Design and maintain production-grade data pipelines and curated datasets that directly support pharmacovigilance activities, including safety monitoring, analytics, and regulatory reporting.
• Ensure data engineering solutions produce reproducible, explainable, and trusted analytics outputs suitable for safety decision support and inspection readiness.
• Enable AI/ML and GenAI workflows for safety analytics, including: Feature engineering and feature store enablement, Embeddings, vectorized representations, and semantic retrieval, Retrieval-Augmented Generation (RAG) patterns for safety analytics tools.
• Own the end-to-end data lifecycle for safety analytics, from source system intake through transformation, serving, and downstream analytical consumption, ensuring data continuity, traceability, and integrity.
• Lead architectural decisions across ingestion, transformation, storage, and serving layers on GCP (e.g., BigQuery, Dataform, object storage).
• Design, implement, and automate scalable, reusable data pipelines and architectures to support evolving safety analytics needs.
• Establish and enforce data quality, validation, lineage, and observability standards for safety analytics datasets.
• Define and implement data governance practices, including data contracts, schema versioning, access control, stewardship, and lifecycle management.
• Ensure safety analytics data and systems meet Global Medical Safety requirements for reliability, auditability, and regulatory use.
• Apply GxP validation expertise to data pipelines, analytics services, and supporting infrastructure.
• Partner with quality and compliance teams to implement CSV/CSA-aligned controls, audit trails, documentation, and organizational change.
• Design and build APIs and microservices-based architectures to operationalize safety analytics and ML capabilities (e.g., feature serving, retrieval services, analytics backends).
• Deploy and operate services on GCP (e.g., Cloud Run, GKE) with a strong focus on security, scalability, and observability.
• Serve as a technical authority and data engineering leader for Safety Analytics within Global Medical Safety.
• Review and influence designs across pipelines, services, feature stores, and AI/ML integrations to maintain a high technical bar.
• Collaborate closely with safety scientists, epidemiologists, biostatisticians, analytics teams, IT, and platform partners to translate safety needs into scalable technical solutions.
• Communicate complex technical concepts and tradeoffs clearly to both technical and non-technical stakeholders.

Principal Data Engineer – Safety Analytics

• Experiment with and combine data from a variety of data sources and with increasing complexity for use in analytics & reporting solutions.
• Own and maintain the MyInsights reporting portal, ensuring analytics and reporting solutions are organized, accessible, and aligned with customer needs and business priorities.
• Supports user training, acts as an ambassador of the solution, and partners with project teams to ensure smooth and launch and adoption.
• Elicit and clarify business needs in order to design and develop appropriate analytics & reporting solutions that satisfy those needs.
• Update and maintain analytics & reporting solutions according to industry standard methodologies.
• Find opportunities to consolidate analytics & reporting solutions.
• Establish and monitor adoption criteria based on utilization statistics.
• Maintain relationships with customer groups and respond to business needs as applicable under the guidance of more senior associates.
• Technical design, build and iteration of analytics & reporting solutions.
• Intakes feedback on prototypes and iterates continuously to improve user experience and customer engagement.
• Partners with IT and other tech vendors and partners for necessary technical development and support as required.
• Proposes mock and prototype low-fidelity designs using design thinking methodology.
• Understands how the solutions in the team work together and finds opportunities to drive consistent service experience across the suite of solutions.
• Work with customer teams to ensure proper insights are extracted from solutions.
• Work in close partnership with more senior Associates, who will provide guidance and technical direction to build the Analyst’s skills.
• Develop more complex solutions, seeking the expertise of more senior associates as needed.

• Develop, maintain, and automate complaint analytics dashboards, reports, and statistical trend analyses to monitor quality performance, identify emerging signals, and support Quality Management Reviews, CAPA activities, and business decision-making.
• Ensure the integrity, accuracy, and governance of complaint and quality system data through validation, reconciliation, audit trail management, and continuous improvement of reporting processes.
• Provide data-driven support for complaint investigations, root cause analysis, risk management activities, and CAPA effectiveness evaluations to strengthen product quality and patient safety outcomes.
• Collaborate with Quality Assurance, Regulatory Affairs, Scientific Application Support, Product Development, Manufacturing, Process Engineering, and other cross-functional stakeholders to identify trends and drive continuous improvement initiatives.
• Support regulatory compliance, internal and external audits, and inspections through accurate reporting, analysis, documentation, and application of quality system requirements.

Senior Quality Engineer – Post Market Data Analyst

• Technical Implementation: Design and implement automated cost monitoring, alerting, and optimization solutions across cloud platforms
• Develop and maintain cost allocation algorithms and tagging strategies for accurate cost attribution
• Build integrations between cloud billing APIs and internal cost management systems
• Create technical documentation and best practices for cost-efficient infrastructure patterns
• Collaborate with platform teams to implement cost optimization recommendations
• Engineering Excellence: Develop tooling and automation to support FinOps processes across fourteen product groups and eleven platform groups
• Build dashboards and reporting mechanisms for real-time cost visibility
• Implement cost guardrails and automated policies to prevent cost overruns
• Design scalable solutions for cost data collection, processing, and analysis
• Contribute to the TechOps Analytics Data Solution FinOps requirements
• Cross-functional Collaboration: Partner with product engineering teams to identify and implement cost optimization opportunities
• Work closely with Infrastructure Foundations Program to align technical solutions with FY27 Cost Reduction Program
• Support FinOps Working Group with technical expertise and implementation guidance
• Collaborate with Data Analytics Team on cost data pipeline development
• Provide technical input for FinOps capability development roadmap
• Continuous Improvement: Research and evaluate new cost optimization tools and techniques
• Contribute to FinOps governance structures and technical standards
• Mentor junior engineers on cost-conscious engineering practices
• Stay current with cloud platform pricing changes and optimization opportunities

Engineer III – Data Analytics

Data Engineer, Business Analyst

• You build and maintain data pipelines in Snowflake and dbt to support reliable, auditable reconciliation between internal and external systems.
• You own and improve the data workflows related to Claim Reimbursement and Contract Sales, ensuring accurate financial tracking and reconciliation with partners.
• You work closely with Accounting, Engineering, Operations, and Reporting to align and reconcile internal and external financial data, acting as a connective link between technical and non-technical teams.
• You ensure data integrity across systems and support internal and external audits through well-documented, traceable data practices.
• You use tools like Thunderclient and Postman to interface with production systems, and help enhance integrations with Service Providers and Merchants to improve the timeliness and accuracy of claim expense reporting and payments.
• You identify, investigate, and fix data quality issues, recommending process improvements to reduce errors and manual intervention, and help extend our data quality standards and operational analytics practices.

• Scope difficult reporting questions and clarify the core business decisions the resulting reports must support
• Audit and deconstruct complex customer marketing reports across legacy BI tools
• Investigate data sources, identity resolution logic, metric definitions, and audience behavior to ensure accuracy
• Train non-technical marketers on how to use AI effectively
• Explain your analytical findings in a clear, actionable way for business decisions
• Design, build, and tune the semantic context layer and document assumptions

Forward Deployed Analytics Engineer

• Design and develop data integrations to deliver Enterprise BI Data Marts and BI Semantic Data Layer(s)
• Develop modern data engineering pipelines leveraging AI assisted capabilities, such as Databricks GenAI features
• Lead multiple projects, provide technical guidance, and support other team members within the DAS organization
• Improve data processes, technology infrastructure, and reporting and analytical services

Lead Analytics Engineer

• Author and maintain dbt models and PySpark transformation jobs, replacing ad-hoc Snowflake scripts with governed, version-controlled, tested code
• Design and implement delivery endpoint configurations as code-customer, delivery target (Snowflake, S3), cadence, cohort filters, incremental and full historical refresh methods
• Write production-grade Python and PySpark for data transformation, validation automation, and delivery pipeline components, including customer-specific data models and schema validation logic
• Coordinate and execute monthly RWD deliveries across all active channel partners: delivery job execution, manifest generation and validation, tokenization workflows, and QC
• Own the channel partner data inquiry queue-triage, investigate, resolve, and communicate on data questions and discrepancies; you are the primary research contact for channel partners
• Follow SDLC best practices: author requirements, write test plans, manage releases, and maintain operating documentation in Confluence
• Leverage AI tools (including Claude Code) to accelerate development, automate documentation, generate and verify code, and improve operational throughput

Analytics Engineer – Life Sciences Delivery Operations

• Design, develop, and maintain data pipelines, integrations, and automated data workflows
• Build and support data lake, data warehouse, Databricks, and analytics platform solutions
• Develop, optimize, and maintain ETL/ELT processes to ingest, transform, and organize data from internal and external systems
• Create and maintain analytics-ready datasets, semantic models, and data structures optimized for Power BI consumption, in collaboration with BI Analysts on semantic model design
• Develop and maintain data transformation workflows using SQL, Python, Databricks, and other modern data platform tools
• Own operational health of the data platform, including monitoring, alerting, incident response, and continuous reliability improvements
• Implement data validation, testing, reconciliation, and monitoring processes to ensure data accuracy and integrity
• Develop and maintain technical documentation, data dictionaries, lineage documentation, and operational procedures
• Collaborate with application developers, BI analysts, and business stakeholders to define data requirements and deliver data solutions
• Establish and promote data engineering standards, best practices, and governance processes
• Evaluate and recommend improvements to data architecture, tools, processes, and technologies
• Administer database and data platform security, including access management, role-based permissions, and auditing of data access
• Ensure data solutions comply with privacy, regulatory, and data governance requirements
• Manage database and platform operations including backup, recovery, disaster recovery planning, and environment management
• Monitor and optimize platform compute utilization and cost, including Databricks cluster configuration and workload efficiency
• Implement data retention, archival, and cataloging policies
• Participate in solution design, architecture reviews, and strategic data initiatives

Data Platform & Analytics Engineer

• own the long-term architecture and evolution of Cribl's analytics engineering platform
• design, build, and maintain certified dbt models as the authoritative source for business-critical metrics
• establish and enforce analytics engineering standards for modeling, testing, documentation, and code review
• design and maintain semantic and metadata layers that enable reliable AI-powered analytics and self-service
• partner with analysts to migrate high-value business logic from Omni into governed warehouse models
• partner with Data Engineering to improve source reliability, warehouse architecture, and Snowflake performance and cost efficiency
• mentor analysts and analytics engineers on dbt development, data modeling, and analytics engineering best practices
• work may happen across many time zones

Staff Analytics Engineer

• Build and own the governed semantic and metrics layer.
• Design and own the semantic layer that is the single source of truth for Tango's metrics.
• Define metrics once, in code, so they resolve consistently across every dashboard.
• Model Tango's data on the modern warehouse.
• Design and build curated, well-documented, tested data models on Redshift.
• Ship embedded, customer-facing analytics on Omni.
• Make the data layer trustworthy for AI.
• Lead the Oracle BI to Omni migration, hands-on.
• Set the standard and level up the team.

• Build and own the governed semantic and metrics layer
• Design and own the semantic layer that is the single source of truth for Tango's metrics
• Model Tango's data on the modern warehouse
• Ship embedded, customer-facing analytics on Omni
• Make the data layer trustworthy for AI
• Lead the Oracle BI to Omni migration, hands-on
• Set the standard and level up the team
• Partner across platform, product, and consumers

• Collects, transforms, and validates data from diverse sources, applying professional analytical techniques to ensure accuracy and reliability.
• Performs exploratory and descriptive analysis to identify trends, patterns, and key performance drivers for business stakeholders.
• Develops and maintains reports, dashboards, and visualizations that communicate analytical findings in a clear and actionable manner.
• Interprets data requirements, collaborates with business partners, and applies professional judgment to structure analyses that meet defined objectives.
• Documents analytical logic, data sources, and methodologies to support transparency, reproducibility, and continuous improvement.
• Supports cross-functional initiatives by providing data-driven insights and contributing to analytical problem-solving efforts.
• Identifies and recommends opportunities to streamline analytical workflows, standardize reporting, or enhance data quality.
• Builds proficiency in analytics tools, data modeling concepts, and best practices to expand analytical independence and effectiveness.

Data Analytics Engineer

• Develop perception algorithms for re-id of vehicles on massive construction sites
• Extend, modify and adapt existing perception signals for analytics needs
• Work with customers to define and scope what is possible with advanced perception systems on-site
• Deploy models and analytics directly to fleets of machines

Machine Learning Engineer – Perception Analytics

• Own end-to-end implementations for the data products for our external customers.
• Set technical direction and standards for the analytics engineering team.
• Build and maintain scalable data pipelines and analytics models using Python, DBT, and SQL.
• Create and manage dashboards in our external and internal analytics products so stakeholders have actionable insights.
• Prototype new data products to add to our product suite.
• Collaborate with engineering to drive the roadmap for the data products.
• Mentor engineers and own their professional development.
• Use GitHub-based workflows to maintain clean, version-controlled analytics code.
• Act as a subject matter expert for analytics best practices.

• Build canonical data models that create a shared source of truth across the company
• Define and maintain core business, operational, financial, product, and customer-facing metrics
• Model data in dbt or equivalent transformation tooling so dashboards, self-serve analytics, and customer reports pull from trusted tables
• Write tests, documentation, and data quality checks that catch issues before they reach users
• Create clear definitions for tables, fields, and metrics so teams understand what the data means and when to use it
• Reconcile metric definitions across internal teams, external reporting needs, and payer customer expectations
• Trace data lineage and debug dashboards, reports, or tables that change unexpectedly
• Partner with analysts, data scientists, operations, finance, product, engineering, and customer-facing teams to understand data needs and translate them into reliable models
• Help build reusable reporting frameworks that make onboarding new payers faster and less manual
• Partner with the data platform team to evolve warehouse tables, improve data architecture, and strengthen data contracts
• Improve warehouse cost, performance, and maintainability
• Support PHI-aware data access patterns and help ensure sensitive healthcare data is modeled and used responsibly

Analytics Engineer jobs in United States

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Senior Analytics Engineer

Commercial Analytics Engineer – Business Decision Intelligence

Senior Engineer, Analytics & Productivity Solutions

Principal Data Engineer – Safety Analytics

Senior Engineer, Analytics & Productivity Solutions

Senior Quality Engineer – Post Market Data Analyst

Engineer III – Data Analytics

Data Engineer, Business Analyst

Analytics Engineer

Forward Deployed Analytics Engineer

Lead Analytics Engineer

Analytics Engineer – Life Sciences Delivery Operations

Data Platform & Analytics Engineer

Staff Analytics Engineer

Staff Analytics Engineer

Staff Analytics Engineer

Data Analytics Engineer

Machine Learning Engineer – Perception Analytics

Staff Analytics Engineer

Senior Analytics Engineer