Analytics Engineer Jobs

About The Position

Requirements

• At least 5 years of experience in analytics engineering, data engineering, or a related technical role, with a track record of shipping and supporting production data solutions.
• Advanced SQL skills and strong data-modeling judgment, including reasoning about join cardinality, aggregation, historical state, and event sequences.
• Substantial hands-on dbt experience, including modular transformations, incremental models, testing, documentation, and dependency management.
• Experience developing LookML views, Explores, dimensions, and measures, diagnosing join behavior, and maintaining consistent metric definitions across reporting experiences.
• Experience with BigQuery or a comparable cloud data warehouse, including query optimization and tradeoffs among freshness, performance, and cost.
• Strong data software-engineering practices, including version control, code review, automated testing, and controlled deployments.
• Working proficiency in Python or Go for scripting, validation, and automation.
• Ability to translate ambiguous business requirements into reusable technical solutions, clarify definitions, explain tradeoffs, and carry implementation through to completion.
• Strong debugging skills and experience tracing data issues across source systems, transformation pipelines, and BI tools.
• Practical experience using AI-assisted development tools and judgment to validate generated SQL, code, and documentation against actual data behavior.

Nice To Haves

• Experience migrating analytical models or BI platforms, including reconciliation, compatibility management, and legacy decommissioning.
• Experience building semantic layers, governed natural-language analytics, or data tools for AI agents, including evaluating the correctness of generated answers.
• Experience preparing reusable datasets for ML or partnering with Applied AI teams on data quality, historical consistency, and feature definitions.
• Experience with GCP, dbt Cloud, orchestration tools such as Airflow or Dagster, or automated data observability.
• Experience working with healthcare or other sensitive data, including access controls, PHI/PII handling, and auditability.

Responsibilities

• Design and implement reusable dbt models and BigQuery datasets that accurately represent core business concepts, with deliberate choices about grain, keys, relationships, and historical behavior.
• Build and maintain LookML views, Explores, measures, and supporting dashboards, keeping business logic consistent across dbt and Looker and documenting definitions for self-service use.
• Implement defined components of data rearchitecture and the Looker Core migration; reconcile results, explain intentional differences, validate downstream dependencies, and help retire legacy models and content.
• Partner with Analytics, Finance, Growth, Customer Success, and Clinical stakeholders to clarify data requirements, definitions, and edge cases, then deliver reusable technical solutions.
• Implement automated tests for model behavior, relationships, freshness, and business rules; investigate discrepancies and production issues and add safeguards against recurrence.
• Improve SQL, materializations, incremental processing, and BigQuery partitioning and clustering; simplify duplicate logic and improve model structure, documentation, and lineage.
• Contribute governed datasets, semantic definitions, and contextual documentation for agents and AI; partner on data-agent implementations and validate answers against trusted queries and metrics.
• Support Applied AI with reliable analytical data and clear interfaces for ML workflows.
• Own work from technical scoping through deployment and iteration, review code, share reusable patterns, and use AI-assisted development tools while maintaining accountability for correctness, privacy, and maintainability.