Data Engineer

Leads a team of data engineers delivering pipelines and platform components across AWS and Azure, balancing hands-on technical work with people leadership. Partners with the Data Architect and business stakeholders to keep the team's roadmap aligned with platform architecture and delivery priorities.

Key Responsibilities:

• Lead and mentor a team of data engineers delivering pipelines across AWS and Azure
• Own technical design and implementation decisions for data pipeline architecture
• Establish and enforce engineering best practices: code quality, testing, CI/CD, documentation
• Plan and prioritize the team's roadmap in collaboration with architecture and business stakeholders
• Troubleshoot and resolve complex data pipeline and platform issues
• Drive adoption of orchestration, monitoring, and data-quality tooling
• Partner with the Data Architect to keep delivered pipelines aligned with platform architecture

Requirements:

• 7+ years of data engineering experience, including 2+ years in a technical leadership role
• Hands-on expertise with both AWS (Glue, EMR, Redshift, Lambda, Kinesis) and Azure (Data Factory, Synapse, Databricks)
• Strong background in pipeline architecture, orchestration (Airflow/ADF), and CI/CD for data
• Experience mentoring engineers and setting technical best practices across a team
• Solid SQL and Python/Scala skills, with experience in distributed data processing (Spark)
• Experience with data quality, testing, and monitoring frameworks
• Strong communication and planning skills; comfortable working directly with stakeholders

Nice to Have:

• Experience with dbt or similar transformation frameworks
• Familiarity with infrastructure-as-code (Terraform, CloudFormation, Bicep)
• Prior experience in a consulting or client-facing delivery environment