Lead Data Engineer

This is a remote position.

About the Role

We are seeking an experienced and hands-on Lead Data Engineer to drive the optimization of our ETL infrastructure while shaping the direction of our data platform. This role requires both technical expertise and leadership skills—balancing execution with collaboration across analysts, engineers, and leadership. You’ll ensure our pipelines are efficient, scalable, and reliable, while enabling automation, governance, and AI-driven analysis for long-term success.

Key Responsibilities

• Own pipeline stability, performance, and scalability across a GCP-based stack (BigQuery, GCS, Dataprep/Dataflow).
  
• Enhance existing ETL workflows for modularity, reusability, error handling, and scalability.
  
• Introduce lightweight data governance practices such as column-level validation, source tracking, and transformation transparency.
  
• Support development of a semantic layer (e.g., KPI definitions, normalized metrics) to streamline downstream analysis.
  
• Collaborate with analysts and dashboard developers to deliver structured, intuitive, and parameterized data outputs.
  
• Partner with leadership to prioritize platform improvements based on business impact and feasibility.
  
• Prepare infrastructure for automated reporting, predictive modeling, and AI-powered insights.
  
• Promote a culture of excellence through documentation, mentoring, and code reviews.
  
• Contribute to team growth through hiring, onboarding, and setting internal standards.
  

Must-Have Skills & Qualifications

• 7+ years of experience in Data Engineering, preferably in fast-paced or multi-client environments.
  
• Strong expertise in Google Cloud Platform (BigQuery, GCS, Cloud Dataprep or Dataflow).
  
• Proficiency in SQL and Python, with a focus on data transformation and reliability.
  
• Proven experience in building and maintaining production-grade ETL pipelines.
  
• Familiarity with metadata-driven development, version control, and task orchestration tools (e.g., Airflow).
  
• Strong communication skills with the ability to explain technical trade-offs to non-technical stakeholders.
  
• Demonstrated ability to balance hands-on execution with team collaboration.
  

Nice-to-Have Skills

• Experience applying data governance principles (lineage tracking, validation frameworks, naming conventions).
  
• Exposure to semantic layer tools (dbt, LookML, etc.).
  
• Familiarity with AI/ML workflows or automated insight-generation tools.
  
• Understanding of marketing/media datasets or attribution modeling.
  
• Prior experience mentoring junior engineers and contributing to process/code standardization.