Job Summary (List Format):- 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, GCPPreferred 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