What are the responsibilities and job description for the Lead Data Engineer position at Recru?
Senior individual contributor focused on designing, building, and hardening data pipelines in a modern Enterprise Data Lakehouse. Lead engineer will drive pipeline stability, performance, and data quality across the platform while operating autonomously and influencing platform standards.
Key Responsibilities:
- Design, build, and optimize data pipelines that integrate diverse sources into a centralized Lakehouse
- Troubleshoot, performance test, and stabilize production data pipelines supporting critical business use cases
- Design and enforce data quality frameworks to ensure correctness, trust, and reliability
- Translate business and operational requirements into scalable technical solutions
- Tune and optimize SQL performance, data models, partitioning, and compaction strategies
- Support and improve platform performance across ingestion, storage, and analytics layers
- Collaborate directly with business leaders, engineers, and operational teams to deliver data‑driven solutions
- Apply strong software engineering practices including testing, version control, CI/CD, and deployment standards
- Help establish technical patterns, standards, and best practices as the data platform evolves
- Mentor other data engineers through technical leadership and collaboration
Qualifications:
- 5 years experience as an AWS Data Engineer designing and supporting data pipelines
- Strong Python and SQL experience, including SQL performance tuning
- Experience owning and supporting production data systems
- Solid software engineering background (development, testing, version control, deployment)
- Experience implementing or working within a Data Lakehouse architecture
- Strong communication skills and comfort working with non-technical stakeholders
Preferred Skills:
- Hands‑on experience with Snowflake (or deep experience with comparable cloud data platforms)
- Experience with AWS and modern data tooling (e.g., Airflow, dbt, Airbyte)
- Familiarity with Kubernetes concepts
- General DevOps exposure and understanding of production deployment models
- Power BI experience
- Background in oil & gas or industrial data environments