Team Lead, Data Analytics

Team Lead, Data Analytics

Position:
Team Lead, Data Analytics - FLSA Status:
Exempt

Location:

Louisville, Kentucky (Remote, Hybrid)
- Reports To:

CTO

eBlu Solutions, a Care Tria Company, was founded in 2012 to help specialty medical practices manage the complex workflow associated with high-cost infusion and injection treatments. Simply put, our solution gets patients to treatment faster.

The Team Lead, Data Analytics is responsible for the day-to-day leadership, technical execution, and operational maturity of eBlu’s data analytics function. This role leads analytics engineers, data engineers, and data architects in designing, building, and supporting modern data pipelines, analytical data models, dashboards, and governed reporting solutions across the organization.

This leader ensures the analytics platform evolves with scalable, secure, and reliable data architecture across Azure SQL, Google Cloud Platform, Big Query, Airbyte, Airflow, dbt, Looker, and Google Data Studio / Looker Studio. The Team Lead balances hands-on technical direction with team coordination, stakeholder partnership, data quality, and delivery discipline to enable trusted business insights and support growth of the platform.

• Lead the design, development, and optimization of analytics solutions across cloud data platforms, including Google Cloud Platform and Big Query.
• Ensure cohesion across source systems, ingestion patterns, transformation logic, semantic models, dashboards, and downstream business reporting.
• Develop and maintain scalable data models, curated datasets, and reusable analytical patterns that support operational, financial, product, and customer-facing reporting needs.
• Contribute to platform modernization roadmaps, technical standards, and build vs. buy recommendations for data tools and services.
• Partner with engineering and platform leaders to reduce data platform technical debt through structured improvements to pipelines, models, documentation, and data contracts.

• Oversee the development and support of reliable batch and scheduled data pipelines using Airbyte, Airflow, dbt, SQL, and cloud-native services.
• Guide ingestion, orchestration, transformation, testing, deployment, and monitoring practices for modern ELT and ETL workflows.
• Establish and improve pipeline observability, data freshness monitoring, failure alerting, lineage, and recovery processes.
• Ensure source-to-report traceability across Azure SQL, Big Query, dbt models, Looker, and Data Studio / Looker Studio assets.
• Promote engineering practices such as version control, peer review, environment management, automated testing, CI/CD, and documentation for data assets.

• Lead delivery of dashboards, reports, metrics, and analytical products using Looker and Data Studio / Looker Studio.
• Translate business goals and operational questions into clear data requirements, metrics definitions, models, and visualizations.
• Partner with Product, Engineering, Operations, Finance, Sales, Customer Success, and executive stakeholders to prioritize high-value analytics work.
• Define and support semantic models, governed metrics, reusable LookML assets, and self-service reporting patterns that improve business adoption and trust.
• Communicate complex data concepts clearly to technical and non-technical audiences, including tradeoffs, risks, assumptions, and recommended next steps.

• Lead, mentor, and coordinate a team of analytics engineers, data engineers, and data architects, ensuring clear priorities, efficient workflow, and consistent execution.
• Support sprint planning, backlog grooming, estimation, roadmap sequencing, and delivery reporting for analytics initiatives.
• Coach team members on technical design, data modeling, SQL quality, pipeline reliability, analytics engineering practices, and stakeholder communication.
• Foster a culture of accountability, continuous improvement, collaboration, knowledge sharing, and responsible experimentation.
• Establish measurable analytics team health metrics, including pipeline reliability, dashboard adoption, data quality, cycle time, incident trends, and stakeholder satisfaction.

• Implement data governance standards that improve data quality, integrity, usability, ownership, and documentation across analytics assets.
• Define and enforce testing practices for dbt models, transformation logic, data reconciliation, and key business metrics.
• Partner with security, compliance, engineering, and business owners to support appropriate access controls, privacy expectations, and regulated-environment requirements.
• Maintain clear documentation for logical and physical data models, source mappings, lineage, refresh schedules, dashboard definitions, and known limitations.
• Ensure analytics practices align with organizational…