Data Engineer

Main requirements

Snowflake Experience

The person should be proactive, well-organized, and an effective communicator, able to independently drive tasks forward, keep processes under control, and communicate clearly with the team and stakeholders.

Requirements

Senior Data Enginer

Snowflake — core development

• Writing and maintaining production views, secured views and stored logic in Snowflake
• Understanding of role-based access control: users, roles, GRANTs, warehouse and database entitlement models
• Enough query-tuning instinct to build views over large fact tables without wrecking performance — partition pruning, clustering awareness, avoiding SELECT *

Microsoft SQL Server

• T-SQL development
• Migrating legacy tables, views and stored procedures to Snowflake

Previous ETL Tooling Experience

Bonus Points

BI and semantic layer

• Sigma Computing — connection setup, workbooks, embedding, AI/Cortex configuration. We are rolling Sigma out now and retiring Tableau Desktop.
• Tableau — data source management, extract vs live strategy, extract scheduling and dependency mapping

Documentation and code review

• Producing data-flow and architecture diagrams
• Reviewing SQL/pipeline code to a standard — we have a code review backlog in other areas because there aren’t enough qualified SQL reviewers. A contractor who can act as a second reviewer is disproportionately valuable.

Azure DevOps (aka ADF Pipelines sync’d with Github)

• CI/CD pipelines for database and ADF artefacts, service principal / client secret configuration, release troubleshooting

External API ingestion

• Consuming and loading third-party APIs into the warehouse (e.g. FX/exchange rate feeds), including auth and error handling
• Snowflake Git integration — connecting a Git repository to Snowflake, versioning database objects and deploying from a branch, plus general source-control discipline for SQL and pipeline code
• Snowflake Streamlit — building lightweight in-warehouse data apps as an alternative to a full BI workbook where the audience is small or the use case is operational
• Python for data engineering and Snowpark
• Snowflake Cortex / LLM-in-warehouse features
• dbt or equivalent transformation tooling (not in use today, but relevant if we modernise)

Working attributes we need

• Comfortable being the second pair of hands on a one-person team — takes a ticket end-to-end without daily direction
• Documents as they go; we are explicitly trying to reduce single-person key-man risk
• Cost-aware: several of our decisions are spend-driven, not purely technical

English - Andvanced