Key Responsibilities

- Lead the technical design and hands on development of scalable, high performance data platforms and solutions, providing technical guidance and direction to the engineering team.

- Contribute to defining and implementing data architecture principles, ensuring alignment with enterprise data strategy.

- Implement enterprise level data engineering standards, frameworks, and patterns while ensuring adherence to security, compliance, and performance best practices.

- Lead the development of highly efficient, fault tolerant, and cost optimized data pipelines across Azure, Snowflake, and AWS.

- Design and build scalable data ingestion pipelines supporting both batch and near real time patterns, leveraging cloud native services across Azure, Snowflake, and AWS.

- Apply layered data modelling patterns (e.g., medallion architecture) to structure and govern data platforms, contributing to architecture evaluation across Data Lakehouse models.

- Implement data governance capabilities including role based access control, dynamic data masking, row level security, and column level policies to protect sensitive data assets.

- Provide technical guidance and hands on expertise to engineering team, ensuring best in class data product development, and mentoring junior engineers.

- Collaborate with security teams to ensure data engineering solutions comply with security and regulatory requirements while implementing data governance best practices.

- Engage with Product Owners, Data Architects, and Engineering teams to deliver scalable and reliable data solutions through agile sprint cycles.

Required Skills





- Hands on experience with Snowflake architecture: virtual warehouses, clustering, time travel, data sharing, and zero copy cloning.

- Proficiency in Snowpark (Python/Java) for advanced transformations and Snowflake Streams & Tasks for CDC and incremental loading.

- Experience with Snowflake governance features: RBAC, data masking, secure views, and object tagging.

- Practical experience with AWS data services: S3, Glue, DMS, Lambda, Kinesis, Step Functions, and IAM.

- Understanding of BYOS/external table patterns, S3 based data lake design, and Snowflake AWS integration via Storage Integrations and external stages.

- Strong SQL skills with experience in complex transformations, window functions, and query optimisation.

- Experience building ELT/ETL pipelines and implementing medallion or equivalent layered data architectures.

- Proficiency with dbt for modular, testable, and version-controlled transformation development.

- Familiarity with Apache Airflow or equivalent orchestration tools for complex pipeline scheduling.

- Experience working with on-premise-to-cloud data migration projects.

- Robust understanding of data security, access control, and compliance requirements.

- Proficiency with Git and CI/CD practices for data engineering workflows.

Qualifications

- Bachelors Degree in Computer Science, Software Engineering, or equivalent professional experience.

- Relevant experience in Data Engineering, building and delivering enterprise scale data solutions, with a strong preference for cloud expertise in Snowflake, Azure, and AWS.

📌 Data Engineer (Delhi)
🏢 Worldclass Tech Talent
📍 Delhi