Lead Data Engineer

Lead and drive data modernization, transformation, governance, and analytics consumption initiatives.

• Build and manage scalable ETL/ELT pipelines for diverse data types (structured, semi-structured, unstructured).
• Develop batch and real-time data processing solutions using modern cloud and big data technologies.
• Design and implement data models, data marts, and analytics-ready datasets for BI, AI, and ML.
• Lead cloud data engineering projects across AWS, Azure, or GCP.
• Ensure data quality, governance, lineage, security, and compliance throughout the data ecosystem.
• Collaborate with business stakeholders, data architects, data scientists, and BI teams to deliver data-driven business value.
• Modernize legacy data warehouses and support data lakehouse architectures and platform transformations.
• Establish and enforce engineering best practices, CI/CD pipelines, Infrastructure as Code (IaC), and automation.
• Mentor and lead data engineering teams; conduct architecture reviews and provide technical governance.
• Optimize platform performance, scalability, reliability, and manage cloud costs.

Proficient in: SQL, Python, PySpark, Spark

• Data modeling (Star Schema, Snowflake, Data Vault)
• ETL/ELT tools: Airflow, DataStage, Informatica, Talend, DBT
• Cloud data warehouses: BigQuery, Snowflake, Synapse, Redshift
• Event streaming: Kafka, Pub/Sub, Event Hub
• DevOps: Git, CI/CD, Terraform, Docker, Kubernetes
• Cloud platforms: AWS, Azure, GCP

Preferred Skills

• Experience with Data Lakehouse (Databricks, Delta Lake)
• Master Data Management (MDM)
• Data Mesh/Data Product Architecture
• AI/ML & Generative AI data platforms
• Domain experience in BFSI, Insurance, Retail, or Healthcare