Data Engineering Lead

What you’ll bring to the table: (Responsibilities)

• Establish and enforce architecture standards that ensure consistent designs that produce repeatable results. 

• Assess the current state of our data infrastructure — evaluate what needs refactoring, and what should be replaced — then execute on that plan with the team.

• Lead the continued build-out of the modern data stack, including ELT pipelines, stream ingestion, transformation logic along with storage and compute — serving as the technical authority when the team needs a decision made.

• Hire and develop data engineering talent, grow the team with engineers experienced in modern ELT architectures and develop the existing team members through mentorship, code review, and technical leadership.

• Define and maintain the data model RFC process, reviewing proposed changes, enforcing boundary discipline, and ensuring no undocumented changes promote to canonical layers.

• Directly lead the data science team, setting priorities, managing workstreams, and translating business problems into clearly scoped DS projects — fostering a culture of accountability, technical rigor, and continuous improvement.

• Own data pipeline monitoring and observability, ensuring production pipelines have appropriate alerting, data quality checks, and incident response processes so failures are caught early and resolved quickly.

• Provide regular visibility into team progress, architectural decisions, and risks to senior leadership, communicating tradeoffs clearly and escalating when business commitments are at risk.

Do you have the right mix of ingredients: (Requirements)

• Bachelor's degree in Computer Science, Engineering, or related field (or equivalent experience)
• 6–10 years of experience in data engineering or a closely related discipline
• 2+ years in a technical lead or staff-level individual contributor role
• Familiarity with data science workflows, ML model lifecycle, and MLOps concepts
• Expertise in data engineering — including model design, testing strategy, incremental patterns, and layer boundary governance
• Experience designing and enforcing data modeling standards in a team environment — not just building models, but establishing the patterns others follow 
• Demonstrated ability to bring a team along technically — through code review, documentation, mentorship, and setting standards that stick 
• Familiarity with CI/CD for data pipelines (GitLab or equivalent) — including automated testing, deployment workflows, and environment promotion strategies 
• Experience with data observability and monitoring — alerting on pipeline failures, data quality degradation, and freshness SLAs (familiarity with tools like Elementary, Monte Carlo, or equivalent) 
• Strong decision-making under ambiguity — this role requires someone who can move forward with incomplete information and course-correct, not someone who needs consensus to proceed 
• Clear, direct communicator who can translate technical tradeoffs for non-technical stakeholders 
• Technical expertise in: Python, Apache Airflow, Apache Kafka, cloud data warehouses (Redshift, GBQ, Databricks)  

Education, Certifications and Experience: Preferred

• Advanced degree in a technical field 
• Experience at a company that has completed a similar legacy-to-modern warehouse migration
• Prior experience managing or mentoring data engineers