Data Engineer

Job description 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. Strong 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.