Lead Data Engineer

Lead data engineering teams and drive data modernization, transformation, governance, and analytics initiatives.
- Build and manage scalable ETL/ELT pipelines for structured, semi-structured, and unstructured data sources.
- Develop both 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 to support reporting, BI, AI, and ML use cases.
- Lead cloud data engineering projects across AWS, Azure, or GCP environments.
- Ensure data quality, governance, lineage, security, and compliance across the organization's data ecosystem.
- Collaborate with business stakeholders, Data Architects, Data Scientists, and BI teams to deliver business value through data products.
- Modernize legacy data warehouses and support the adoption of data lakehouse architectures and platform transformations.
- Establish engineering best practices, including CI/CD pipelines, Infrastructure as Code (IaC), and automation standards.
- Mentor and lead Data Engineers, conduct architecture reviews, and enforce technical governance.
- Optimize data platform performance, scalability, reliability, and control cloud costs.
Core Technical Skills:
- SQL, Python, PySpark, Spark
- Data Modeling (Star Schema, Snowflake, Data Vault)
- ETL tools: Airflow, DataStage, Informatica, Talend, DBT
- Cloud Data Warehouses: BigQuery, Snowflake, Synapse, Redshift
- Streaming: Kafka, Pub/Sub, Event Hub
- DevOps: Git, CI/CD, Terraform, Docker, Kubernetes
- Cloud Platforms: AWS, Azure, GCP
Preferred Skills:
- Data Lakehouse (Databricks, Delta Lake)
- Master Data Management (MDM)
- Data Mesh/Data Product Architecture
- AI/ML & GenAI Data Platforms
- Experience in BFSI, Insurance, Retail, or Healthcare domains