Lead Data Engineer

Role Overview

We are seeking an experienced Lead Data Engineer to drive the design and delivery of scalable, reliable, and cost-efficient data platforms. This role requires deep, hands-on expertise across the modern data engineering stack — distributed data processing, data modeling, ETL/ELT pipeline development, and workflow orchestration — along with a solid understanding of Large Language Models (LLMs) and their data requirements.

What You Will Do

Lead the design and development of scalable, high-performance data pipelines and platforms across batch and streaming workloads, architect and maintain ETL/ELT workflows, define and govern data models, and establish engineering best practices.

Why It Might Be a Fit

The ideal candidate will combine strong engineering fundamentals with technical leadership, mentoring engineers, setting best practices, and partnering with architects, data scientists, and business stakeholders to enable advanced analytics and GenAI use cases.

Requirements

• 10+ years of hands-on data engineering experience
• Strong, hands-on expertise with the Databricks Lakehouse Platform
• Proven experience designing and implementing the medallion (bronze/silver/gold) architecture
• Strong expertise in distributed data processing using Apache Spark
• Proven experience designing and building ETL/ELT pipelines
• Expert-level proficiency in Python and SQL
• Strong experience building and managing data lakes and data warehouses
• Working knowledge of Large Language Models (LLMs) and GenAI concepts
• Hands-on experience with at least one major cloud platform
• Proven experience leading large-scale data migrations
• Strong understanding of distributed data processing, partitioning, and performance optimization techniques
• Experience implementing data security, governance, lineage, and access control
• Strong understanding of object-oriented programming, software design patterns, and CI/CD practices