Data Engineering Manager

8+ years of professional experience in data engineering or software engineering, with a deep understanding of distributed data systems and modern data architecture. 2+ years of experience managing engineering teams, including performance management and leading senior engineers. Proven experience building and operating large-scale, cloud-based data platforms, ideally in AWS (S3, Glue, IAM, CLI, etc.) or equivalent environments. Hands-on experience with SQL and Python, and a strong understanding of ETL/ELT workflows and data lifecycle management. Deep experience with data warehouse and data lakehouse technologies such as Snowflake, Redshift, BigQuery, and Trino as the primary compute infrastructure. Proficiency with workflow orchestration tools (Airflow, Dagster, Prefect, or Flyte) and data integration tools (Fivetran, Stitch, Airbyte, Meltano, or Glue). Experienced with infrastructure-as-code (Terraform or CloudFormation) and deployment automation. Comfortable with Docker and Kubernetes, deploying and scaling data-intensive workloads in containerized environments. Excellent communication and collaboration skills, able to work effectively across technical and business teams.

Lead and mentor a team of experienced data engineers, supporting their growth, performance, and technical development. Design and deliver large-scale, reliable, and cost-efficient data pipelines and platforms that power analytics, reporting, and data-driven products. Own and evolve the core compute data infrastructure built on Trino, ensuring scalable, performant, and cost-optimized query processing across the data lakehouse environment. Oversee major data migrations from our warehouse systems to modern, cloud-native lakehouse data platforms with minimal disruption. Drive the evolution of our data lakehouse and warehouse ecosystems, optimizing storage, compute, and orchestration for performance and cost efficiency. Collaborate closely with Product, Analytics, and Engineering teams to deliver data infrastructure that enables trustworthy and timely insights. Champion best practices in data modeling, governance, and observability to ensure data quality, discoverability, and reliability across the organization. Contribute to infrastructure and automation, leveraging tools like Terraform, Docker, Kubernetes, and CI/CD pipelines to ensure consistency, scalability, and resilience. Stay technically engaged, participating in architectural design, reviewing implementations, and guiding the team through complex technical decisions. Leverages AI tools (e.g. Copilot, Cursor, Claude Code) to improve coding speed and debug faster.