Lead Cloud Data Engineer (Must be 15+ Years Experience)

Locatons: Boston Office ( 1^st Choice) , Portsmouth NH, Plano Tx, Indianapolis IN 2 days from Office.

• Experience: Minimum of 15 years of experience in development with 7+ years of experience in data engineering, with a proven track record of designing, building, and optimizing scalable data pipelines and architectures
• Expert-level proficiency in SQL and strong experience in data transformation, automation, and orchestration
• SQL: Expert use of SQL for querying and managing data, including complex joins, subqueries, window functions, and performance tuning.
• Snowflake: Advanced use of Snowflake Cloud Data Platform for data warehousing, including Snowflake SQL, schema design, Snowpipe, streams/tasks, and integration capabilities.

Job Description

• Experience: Minimum of 13 years of experience in development with 7+ years of experience in data engineering, with a proven track record of designing, building, and optimizing scalable data pipelines and architectures
• Expert-level proficiency in SQL and strong experience in data transformation, automation, and orchestration
• Deep understanding of data modeling, data warehousing, and data architecture best practices
• Hands-on Experience with modern public cloud-based data platforms Snowflake (preferable), AWS, Azure
• Advanced proficiency in AWS services including (but not limited to): S3, Glue, Lambda, EC2, and Athena
• Proficient with ETL/ELT data pipelines, patterns for loading Data Warehouses, Lakes
• Solid experience with DevOps practices and automation frameworks, including CI/CD pipelines, GitHub Actions, Bamboo, and pipeline-as-code principles
• Skilled in BI/reporting tools (e.g., Power BI, Tableau)
• Tools/Technologies
• SQL: Expert use of SQL for querying and managing data, including complex joins, subqueries, window functions, and performance tuning.
• Snowflake: Advanced use of Snowflake Cloud Data Platform for data warehousing, including Snowflake SQL, schema design, Snowpipe, streams/tasks, and integration capabilities.
• Data Warehousing & Modelling: Dimensional modeling techniques, star and snowflake schemas, fact and dimension table design, ETL/ELT frameworks, and general data architecture best practices.
• GitHub Actions & CI/CD: Git and GitHub for version control, with GitHub Actions for automating CI/CD pipelines (testing, deployment workflows, environment management).
• Power BI: Microsoft Power BI for creating data visualizations and dashboards, basic report development and publishing processes.
• Python: Scripting with Python for automation and data manipulation tasks (using libraries like pandas for simple transforms, if required).
• DevOps/Other Tools: Familiarity with development and DevOps tools such as Docker (for containerizing data tools), monitoring tools, and any relevant IDEs or project management tools to facilitate efficient delivery (optional, as needed per project).