Senior Analytics Engineer

Position: Senior Analytics Engineer

Location: Louisville, Kentucky (Remote, Hybrid)

FLSA Status: Exempt

Job Summary

The Senior Analytics Engineer is responsible for designing, developing, and supporting trusted analytical data models, modern data pipelines, and business intelligence assets that enable high-quality reporting and decision-making across CareTria Patient Access Solutions. This role serves as a senior technical contributor within the Data Analytics function, partnering closely with analytics engineers, data engineers, data architects, product teams, engineering teams, and business stakeholders.

This position requires strong hands-on experience with Azure SQL, Google Cloud Platform, BigQuery, Airbyte, Airflow, dbt, Looker, and Google Data Studio / Looker Studio. The Senior Analytics Engineer will translate business needs into scalable data models, governed metrics, reliable ELT/ETL workflows, and well-documented analytics solutions that support operational, financial, product, customer, and executive reporting needs.

The role balances analytics engineering execution, data quality ownership, stakeholder enablement, and technical mentorship while helping modernize eBlu s data platform and improve confidence in enterprise data assets.

Key Responsibilities

Analytics Engineering & Data Modeling

- Design, build, test, and maintain curated analytical datasets, dimensional models, semantic models, and reusable data assets using SQL, dbt, BigQuery, Azure SQL, and related cloud data services.
- Translate business requirements into clear metrics definitions, transformation logic, source mappings, and scalable data models that support accurate reporting and self-service analytics.
- Develop and maintain dbt models, tests, documentation, lineage, macros, and reusable analytics engineering patterns.
- Optimize SQL, BigQuery queries, Azure SQL extracts, and transformation workflows for performance, reliability, maintainability, and cost efficiency.
- Ensure source-to-report traceability across operational systems, Azure SQL, GCP, BigQuery, dbt models, Looker,



and Data Studio / Looker Studio assets.

Modern Data Pipeline Development & Operations

- Build, enhance, and support modern batch and scheduled data pipelines using Airbyte, Airflow, dbt, SQL, and cloud-native data services.
- Develop ingestion, orchestration, transformation, testing, deployment, monitoring, and recovery practices for reliable ELT and ETL workflows.
- Partner with data engineering and platform teams to integrate source systems, improve data freshness, and reduce manual reporting processes.

- Implement pipeline observability, data quality checks, freshness monitoring, failure alerting, and operational documentation.
- Support CI/CD, version control, peer review, setting management, and release practices for data models and pipeline code.

Business Intelligence & Stakeholder Enablement

- Design, build, and support dashboards, reports, governed metrics, and analytical products using Looker and Google Data Studio / Looker Studio.

- Partner with Product, Engineering, Operations, Finance, Sales, Customer Success, and leadership stakeholders to understand analytical needs and deliver actionable insights.

- Define and support semantic models, LookML assets, dashboard standards, and self-service reporting patterns that improve usability and trust.

- Communicate data definitions, assumptions, limitations, tradeoffs, and recommendations clearly to both technical and non-technical audiences.

- Help stakeholders interpret data trends, investigate discrepancies, and improve data-driven decision-making.

Data Quality, Governance & Documentation

- Own data quality practices for assigned subject areas, including validation rules, reconciliation checks, automated tests, issue triage,



and root-cause analysis.

- Document logical and physical data models, metric definitions, source mappings, transformation logic, lineage, refresh schedules, and known limitations.

- Contribute to data governance standards that improve data quality, integrity, ownership, usability, privacy, and access control.

- Partner with security, compliance, engineering, and business owners to ensure analytics assets align with regulated-environment expectations and organizational standards.

- Promote consistent naming conventions, development standards, documentation practices, and review processes across analytics assets.

Technical Leadership & Continuous Improvement

- Serve as a senior technical resource for analytics engineering design, SQL quality, dbt best practices, modeling decisions, dashboard development, and pipeline reliability.

- Mentor analytics engineers and analysts through code reviews, design guidance, documentation standards, and practical knowledge sharing.

- Contribute to analytics platform modernization roadmaps, technical standards, backlog refinement, estimation, and delivery planning.

- Identify opportunities to reduce technical debt, automate recurring analytics work, improve pipeline resiliency, and increase stakeholder confidence in data products.

- Stay current on modern data stack practices, cloud data warehousing, healthcare analytics, analytics engineering, orchestration, BI tools, and responsible AI-enabled automation opportunities.

Experience, Skills & Qualifications

Education

- Bachelor s degree in Computer Science, Data Science, Information Systems, Statistics, Engineering, or a related technical field required.

- Master s degree, equivalent experience, or relevant professional certifications preferred.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

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