- Design, develop, and maintain end-to-end ETL/ELT pipelines using Databricks (PySpark) and Snowflake to enable efficient and scalable data processing.

- Build and manage secure and scalable data platforms using PostgreSQL and DynamoDB, tailored to meet specific application and business requirements.

- Develop both real-time and batch data ingestion pipelines from a variety of sources including APIs, logs, files, and traditional databases.

- Apply complex transformation logic to clean, enrich, and normalize data, ensuring high data quality for analytics and reporting purposes.

- Optimize data pipelines and storage solutions by leveraging best practices such as partitioning, indexing, and query tuning to improve performance and reduce costs.

- Implement robust data governance frameworks including data quality checks, access controls, backup strategies, and

compliance policies.

- Lead the data strategy to unify and modernize large-scale existing data platforms and architectures.

- Establish and enforce data architecture principles, standards, and reusable design patterns to ensure consistency across teams and projects.

- Design and oversee enterprise data architectures including data lakes, data warehouses, and data mesh models.

- Collaborate closely with engineering teams to provide guidance on ingestion, transformation, storage strategies, and implement CI/CD best practices for production-grade data pipelines.

Required Skills & Qualifications :

- 10+ years of proven experience in data architecture and engineering with a focus on ETL/ELT pipeline design and cloud data platforms.

- Hands-on expertise with Databricks (PySpark) and Snowflake for scalable data processing and warehousing.

- Strong knowledge of relational and NoSQL databases including PostgreSQL and DynamoDB.

- Experience building both real-time and batch ingestion pipelines from heterogeneous data sources.

- Proficient in data transformation, cleansing, and normalization techniques for analytical workloads.

- Deep understanding of performance tuning techniques such as partitioning, indexing, and query optimization.

- Experience implementing data quality frameworks, access controls, backup, and governance best practices.

- Demonstrated ability to lead data strategy and unify complex data environments.

- Expertise in designing and managing data lakes, warehouses, and emerging data architectures like data mesh.

- Familiarity with CI/CD pipelines, version control, and deployment best practices in data engineering contexts.

- Strong analytical, problem-solving, and communication skills with the ability to collaborate across technical and business teams.

Preferred Qualifications :

- Experience working in agile environments and collaborating with cross-functional teams.

- Knowledge of cloud platforms and services related to data processing and storage (AWS, Azure, GCP).

- Prior experience in data governance and compliance frameworks (GDPR, HIPAA, etc.).