Data Engineer

BlueCloud is a Snowflake Elite Partner and the 2026 CoCo Catalyst Snowflake Partner of the Year. We help enterprise organizations move from fragmented legacy systems to unified, AI-ready Snowflake platforms — delivering data migration, engineering, governance, BI & analytics, and AI/ML solutions 40–50% faster than traditional approaches.

With 450+ Snowflake consultants, 200+ enterprise transformations under our belt, and a 100% Snowflake focus, we combine advisory-led thinking with AI-powered accelerators to turn months of work into weeks of results. Our clients span Financial Services, Healthcare & Life Sciences, Retail, Manufacturing, Energy, and more — and the outcomes speak for themselves: 97% faster reports, 40% fraud reduction, $1.5M in client savings, and 10× client growth.

We don't just strategize — we execute.

About the Role

BlueCloud is seeking an experienced Senior Snowflake Data Engineer to design, build, and optimize scalable data solutions within complex enterprise environments. This is a hands-on engineering role focused on developing reliable data pipelines, improving Snowflake performance and cost efficiency, and modernizing existing data-processing workloads.

You will collaborate with data architects, analysts, DevOps engineers, and client stakeholders to deliver secure, maintainable, and high-performing data platforms. The ideal candidate combines strong Snowflake expertise with advanced SQL and Python skills and has experience delivering data solutions in cloud-based enterprise environments.

Key Responsibilities

• Design, develop, and maintain scalable data pipelines and transformation workflows within Snowflake.

• Build and optimize ELT/ETL processes using SQL, Python, Snowpark, dbt, and Snowflake-native capabilities.

• Modernize existing Python- and stored-procedure-based pipelines using set-based processing, incremental loading, and reusable engineering patterns.

• Implement Snowflake-native data-processing solutions using Dynamic Tables, Streams, Tasks, and stored procedures.

• Improve platform performance and cost efficiency through query tuning, warehouse right-sizing, clustering strategies, workload isolation, and auto-suspend and auto-resume policies.

• Develop reliable orchestration, dependency management, monitoring, error handling, retry, and recovery mechanisms.

• Design and maintain dimensional, relational, and domain-oriented data models.

• Implement data-quality checks, observability, testing, lineage, and documentation standards.