Data Engineer Lead

Responsibilities

• Design, build, and scale modern data platforms (data lakes, warehouses, lakehouse architectures)
• Develop and maintain robust ETL/ELT pipelines across batch and real-time data sources
• Enable AI/ML and GenAI use cases by building data pipelines, feature stores, and supporting model productionization
• Remain hands-on in coding (Python, SQL, Spark), architecture design, and troubleshooting complex data issues
• Architect and manage cloud-based data platforms (AWS, Azure, or GCP) using modern tools (e.g., Databricks, Airflow, Kafka, dbt)
• Lead and mentor a team of data engineers, setting technical standards and best practices
• Collaborate with cross-functional teams (AI, product, business) to translate requirements into scalable data solutions
• Establish data governance, lineage, security, and ensure compliance with regulatory standards

Requirements

• 8-12+ years of experience in data engineering, with a proven track record of building and scaling production-grade data platforms
• Strong hands-on experience in Python, SQL, and distributed data processing frameworks (e.g., Spark)
• Experience with cloud platforms (AWS, Azure, or GCP) and modern data stack tools (Databricks, Airflow, Kafka, dbt)
• Exposure to AI/ML workflows and familiarity with GenAI concepts (e.g., LLMs, RAG pipelines)
• Proven experience leading teams while remaining hands-on in technical delivery
• Strong understanding of data architecture, data modeling, and data governance frameworks
• Excellent problem-solving skills and ability to operate in a fast-paced, evolving environment
• Strong communication skills with the ability to engage both technical and non-technical stakeholders