Lead Data Engineer

Key Responsibilities

• Design end-to-end data architectures for client engagements including ingestion transformation modeling warehousing and analytics delivery.

• Lead the technical execution of client-facing and internal data projects maintaining accountability for timelines scope and quality.

• Provide subject-matter expertise across the data engineering lifecycle including hands-on support for complex technical issues and performance optimization.

• Facilitate architecture and design reviews articulating options trade-offs and recommendations to technical and business audiences.

• Establish and enforce engineering standards controls and audit procedures that safeguard data accuracy integrity and timeliness.

• Define and promote AI-augmented development practices including structuring projects for effective use of coding agents and coaching team members in these workflows.

• Partner with data scientists analysts business stakeholders and practice leadership to align technical delivery with client objectives and the teams broader technology roadmap.

• Mentor engineers encourage knowledge sharing and contribute to a culture of continuous improvement across the practice.

• Identify and implement improvements to delivery processes tooling and methodology.

• Design and implement DevOps and SDLC strategies and best practices for customers.

  Qualifications

  Required

• Bachelors or Masters degree in Computer Science Engineering or a related technical discipline.

• 810 years of professional experience in data engineering including demonstrated success designing and delivering production data solutions.

• Expertise in data modeling ETL/ELT development and data integration.

• Hands-on experience with at least one major cloud platform (AWS Azure or GCP) and modern data warehousing technologies.

• Advanced proficiency in SQL and Python; experience with Spark or comparable distributed processing frameworks.

• Prior technical leadership experience with the ability to direct and develop engineering teams.

• Excellent analytical problem-solving and communication skills with the ability to engage effectively with clients executives and technical staff.

• Ability to balance technical rigor with business priorities budgets and client constraints.

• Experience architecting and implementing SDLC and DevOps best practices including but not limited to version control deployment pipelines Infrastructure as Code (IaC) Azure DevOps GitHub Actions GitLab Terraform and Declarative Automation Bundles

  Preferred

• Active day-to-day use of AI-assisted development tools (e.g. Cursor Claude Code Codex) as part of a professional engineering workflow.

• Experience building agentic pipelines retrieval-augmented generation (RAG) systems or LLM-powered data and analytics products.

• Prior experience in a consulting or professional services environment.