Lead Data Engineer

Role Overview:

This is a senior individual contributor role focused on designing, building, and hardening data pipelines within a modern Enterprise Data Lakehouse environment. The primary driver for this hire is improving pipeline stability, performance, and data quality across the platform.

The “Lead” designation reflects technical depth and ownership—not people management. You will operate with high autonomy, influence platform standards, and collaborate closely with both technical teams and business stakeholders.

Key Responsibilities:

• Design, build, and optimize new and existing data pipelines integrating diverse data sources into a centralized Data Lakehouse
• Troubleshoot, performance test, and stabilize production data pipelines that support critical business use cases
• Design and enforce data quality frameworks to ensure data 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
• Work 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 continues to evolve
• Mentor other data engineers over time through technical leadership and collaboration

Data Platform & Architecture

• Modern lakehouse architecture (active modernization, not greenfield)
• Apache Iceberg / Delta Lake concepts in use
• Snowflake as the primary analytics platform
• S3-based object storage
• Data pipelines consumed by data engineers and data scientists across the organization

Required Qualifications:

• 5+ years of 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 / Nice to Have:

• 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 (hands-on not required)
• General DevOps exposure and understanding of production deployment models
• Power BI experience
• Background in oil & gas or industrial data environments