Azure​/Snowflake Lead

Azure/Snowflake Lead

Systems Engineering Services is seeking an Azure/Snowflake Lead based out of the Chicago, IL area.

• 8+ years overall experience in Data Engineering / Data Warehousing / Analytics.
• 5+ years hands‑on experience with Snowflake in production environments.
• Proven experience leading at least one large end‑to‑end migration from on‑prem / legacy DWH to Snowflake on Azure.
• Strong experience with Azure cloud services:
  Azure Data Factory, Data Lake Storage (ADLS), Azure Databricks and/or Synapse, Key Vault, Azure Dev Ops or Git Hub.

• Lead the end‑to‑end architecture and design of Snowflake‑based data platforms on Azure, including integration with Azure services (ADF, Synapse pipelines, Azure Functions, Key Vault, ADLS, etc.).
• Define and implement data modeling standards (star/snowflake schema, data vault, dimensional modeling) tailored for analytics, BI, and downstream data products.
• Design secure, scalable, and cost‑efficient Snowflake environments, including warehouses, databases, schemas, roles, resource monitors, and virtual warehouses.
• Lead migration strategy and roadmap for moving data from legacy/on‑prem systems to Snowflake on Azure.
• Work with stakeholders to assess current state (source systems, ETL, reporting, data quality) and design target‑state architecture on Snowflake.
• Define migration waves/phases, including data profiling, schema conversion, historical load, incremental load, and cutover strategy.
• Oversee and implement data ingestion pipelines from various sources (databases, flat files, APIs, streaming) into ADLS / landing zones and then into Snowflake using tools such as Azure Data Factory, Synapse pipelines, or Databricks, plus CDC where applicable.
• Manage data reconciliation and validation to ensure completeness, accuracy, and performance parity (or improvement) compared to legacy platforms.
• Lead a team of data engineers / ETL developers delivering Snowflake‑based solutions and migration work streams.
• Define and enforce coding standards, code review practices, and CI/CD pipelines for Snowflake objects (SQL, stored procedures, views, tasks, streams).
• Design & build ELT/ETL patterns (staging / raw / curated / semantic layers), using tools such as dbt, ADF, Synapse, Databricks, or other orchestration tools.
• Implement automated testing frameworks (unit tests, regression tests, data quality checks) and monitoring (SLAs).
• Monitor query performance and optimize Snowflake workloads using query profiling, clustering, partitioning, and warehouse sizing strategies.
• Implement resource monitors, auto‑scaling, and auto‑suspend policies to optimize compute usage and manage Snowflake consumption costs.

Jake Lutman
Technical Recruiter
Systems Engineering Services