Senior Data Engineer

Top Required Skills

• 10+ years of experience in data engineering / analytics engineering roles.
• Strong hands-on experience with dbt in production, including:
• Model development and dependency management
• Macro development and reusable frameworks
• Testing strategies (schema tests, custom tests)
• Deployment and environment management
• Strong Snowflake expertise, including:
• Data modeling and warehouse design
• Performance tuning and cost optimization
• Deep understanding of virtual warehouses, micro-partitions, clustering, and query pruning
• Role-based access control (RBAC) and secure data access
• Advanced SQL expertise with ability to build and optimize complex transformations.
• Strong Python programming skills for data engineering use cases.
• Proven experience with Git integration, including collaborative development workflows.
• Strong experience implementing CI/CD pipelines for data platforms and dbt deployments.
• Experience building and maintaining production-grade data pipelines with SLAs, monitoring, and reliability standards.
• Mandatory requirement: direct, hands-on experience with Fivetran, dbt, and Snowflake in production, including Fivetran connector setup and troubleshooting, dbt model development and testing, and Snowflake data engineering, performance tuning, and secure access patterns. This is a strict screening criterion.

Key Responsibilities

• Design, build, and maintain scalable ELT pipelines, leveraging Fivetran for ingestion and dbt for transformation on Snowflake. Direct hands-on experience with Fivetran, dbt, and Snowflake is required for this role.
• Develop and maintain robust dbt projects, including:
• Modular models (staging, intermediate, marts)
• Reusable macros and Jinja templating
• Snapshots for SCD Type 2 handling
• Schema and custom data quality tests
• Documentation using dbt docs
• Implement modular and reusable dbt architecture supporting multi-environment deployments (dev, test, prod).
• Design and implement scalable data models using best practices (dimensional modeling, star schema, and data vault where applicable).
• Optimize Snowflake performance and cost efficiency, including:
• Query tuning and execution optimization
• Warehouse sizing and workload management
• Effective use of micro-partitions, clustering, and pruning
• Build and enforce strong data quality and validation frameworks, including:
• Unit testing for transformations (dbt and custom frameworks)
• Data reconciliation and consistency checks
• Develop Python-based solutions for automation, orchestration support, metadata-driven processing, and operational tooling.
• Implement and enforce Git-based development practices:
• Version control, branching strategies, pull requests, and code reviews
• Congestive and collaborative engineering workflows
• Build, maintain, and enhance CI/CD pipelines for dbt deployments:
• Automated build, test, and deployment processes
• Environment promotion (dev ? test ? prod)
• Integration with enterprise deployment pipelines
• Work with orchestration tools such as Airflow / Astronomer to schedule, monitor, and manage data pipeline execution (preferred).
• Collaborate closely with platform, governance, and business teams to align on data requirements, access control, and delivery expectations.