Job
Description
Role Overview: As a hands-on Data Engineer at BuildingBlocks, you will be responsible for owning the data layer of customer go-lives. Your main tasks will include ensuring validated data migrations, strengthening analytics pipelines, and powering business dashboards with accurate and performant data. Your role will involve validating end-to-end data migrations, constructing high-quality SQL models, and setting up automated data quality checks to identify issues early on. This position is highly technical and impact-driven, focusing on migration testing, SQL performance tuning, and data quality automation following AWS and industry best practices. Key Responsibilities: - End-to-End Migration Validation: Design and execute functional and performance validation for data migrations, including parity, nullability, PK/FK, duplication, and sampling checks. Provide complete documentation and sign-off aligned with AWS migration testing guidelines. - Advanced SQL Development: Compose and optimize analytical SQL queries (CTEs, window functions, incremental loads). Utilize EXPLAIN plans to fine-tune query performance and ensure that indexes and statistics support BI workloads. - Automated Data Quality Frameworks: Implement and maintain data validation frameworks using tools like Great Expectations, Deequ, or similar. Automate validation processes and publish Data Docs for transparency across teams. - Modeling & Documentation (dbt): If leveraging dbt, build models with tests, exposures, and documentation to ensure traceability between dashboards and upstream data sources. - Orchestration & Reliability: Operationalize data validation and transformation jobs within Airflow DAGs. Ensure well-defined SLAs, alerts, and reliable pipeline operations. - (Optional) Cloud Data Engineering: Develop incremental pipelines and optimize batch processing for Snowflake (Streams & Tasks) or PostgreSQL, focusing on performance and cost efficiency. Minimum Qualifications: - Experience: You should have 4-7+ years of experience as a Data Engineer or Analytics Engineer. - SQL Expertise: Demonstrate advanced proficiency in SQL and strong RDBMS fundamentals (PostgreSQL required). Showcase experience in query tuning using EXPLAIN/analyze. - Migration Validation: Hands-on experience in designing and executing data migration validation, covering aspects like parity, integrity, and performance testing. - Tooling Knowledge: Familiarity with tools like dbt, Great Expectations, Deequ/PyDeequ, and Airflow. - Version Control: Comfortable with Git-based workflows and CI/CD integration. Nice to Have: - Experience with Snowflake (Streams, Tasks, cost optimization, and warehouse tuning). - Exposure to BI tools such as Looker, Power BI, Tableau, or Metabase. - Working knowledge of Python for lightweight data transformations and validation frameworks. Role Overview: As a hands-on Data Engineer at BuildingBlocks, you will be responsible for owning the data layer of customer go-lives. Your main tasks will include ensuring validated data migrations, strengthening analytics pipelines, and powering business dashboards with accurate and performant data. Your role will involve validating end-to-end data migrations, constructing high-quality SQL models, and setting up automated data quality checks to identify issues early on. This position is highly technical and impact-driven, focusing on migration testing, SQL performance tuning, and data quality automation following AWS and industry best practices. Key Responsibilities: - End-to-End Migration Validation: Design and execute functional and performance validation for data migrations, including parity, nullability, PK/FK, duplication, and sampling checks. Provide complete documentation and sign-off aligned with AWS migration testing guidelines. - Advanced SQL Development: Compose and optimize analytical SQL queries (CTEs, window functions, incremental loads). Utilize EXPLAIN plans to fine-tune query performance and ensure that indexes and statistics support BI workloads. - Automated Data Quality Frameworks: Implement and maintain data validation frameworks using tools like Great Expectations, Deequ, or similar. Automate validation processes and publish Data Docs for transparency across teams. - Modeling & Documentation (dbt): If leveraging dbt, build models with tests, exposures, and documentation to ensure traceability between dashboards and upstream data sources. - Orchestration & Reliability: Operationalize data validation and transformation jobs within Airflow DAGs. Ensure well-defined SLAs, alerts, and reliable pipeline operations. - (Optional) Cloud Data Engineering: Develop incremental pipelines and optimize batch processing for Snowflake (Streams & Tasks) or PostgreSQL, focusing on performance and cost efficiency. Minimum Qualifications: - Experience: You should have 4-7+ years of experience as a Data Engineer or Analytics Engineer. - SQL Expertise: Demonstrate advanced proficiency in SQL and strong RDB
