Data Architect

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 Data Architect to lead the assessment, design, and modernization of enterprise data platforms, with a strong focus on Snowflake. This role combines architecture leadership, hands-on platform expertise, and direct client engagement.

You will define target-state architectures, assess existing data estates, establish modernization roadmaps, and guide multidisciplinary delivery teams through implementation. You will also help clients create secure, governed, cost-efficient, and AI-ready data platforms that support both current analytics requirements and future AI and context-layer initiatives.

The ideal candidate is a trusted technical advisor who can translate complex business requirements into practical architecture solutions and communicate effectively with engineering teams, business stakeholders, and executive sponsors.

Key Responsibilities

• Lead the architecture, assessment, and modernization of enterprise Snowflake data platforms.

• Define current-state and target-state architectures across data ingestion, transformation, storage, orchestration, governance, security, and consumption.

• Lead inventories and assessments of Python-, SQL-, and stored-procedure-based pipelines, classifying workloads by business criticality, frequency, volume, runtime, reliability, and cost.

• Analyze Snowflake usage telemetry to establish cost and performance baselines across pipelines, warehouses, and workloads.

• Identify architectural and engineering anti-patterns, including procedural row-by-row processing, unnecessary full refreshes, missing incremental patterns, redundant transformations, warehouse mis-sizing, and insufficient workload isolation.

• Recommend modernization strategies using set-based processing, incremental data patterns, Dynamic Tables, Streams and Tasks, Snowpark, dbt, and appropriate orchestration technologies.