Snowflake Engineer

SUMMARY

Design, build, and maintain robust ETL/ELT pipelines feeding a Snowflake-based data platform

     Build and manage integrations using SnapLogic to connect source systems, APIs, and downstream consumers

     Develop and maintain data models and transformations in dbt, including tests, documentation, and CI/CD-based deployment

     Design dimensional and/or medallion-style (Bronze/Silver/Gold) data architectures that balance performance, cost, and usability

     Use AI-assisted tools to accelerate development generating boilerplate code, drafting SQL/dbt models, writing documentation, debugging pipeline failures, and summarising data quality issues

     Partner with data quality, governance, and analytics teams to ensure data is well-modelled, well-documented, and trustworthy

     Optimise Snowflake warehouse performance and cost (query tuning, clustering, resource monitors)

     Write clean, tested, version-controlled code and contribute to CI/CD pipelines

     Mentor junior engineers, including on how to use AI tools responsibly and effectively (e.g., reviewing AI-generated code, not blindly trusting output)

     Contribute to internal standards for prompt patterns, reusable AI workflows, or tooling that make the whole team faster

CORE SKILLS

     Snowflake strong hands-on experience with data modelling, performance tuning, security/access, and cost management

     SnapLogic building and maintaining integration pipelines and connecting heterogeneous source systems

     dbt writing modular, tested transformations; managing dependencies, macros, and documentation

     Data Modelling dimensional modelling, medallion/layered architectures, normalisation vs. denormalisation trade-offs

     Strong SQL and at least one scripting language (Python preferred)

     Familiarity with orchestration tools (Airflow, ADF, or similar)

     Working knowledge of git-based CI/CD workflows

AI - AUGMENTED WORKING STYLE (WHAT WE'RE LOOKING FOR)

     Regularly uses AI coding assistants (Copilot, Claude Code, Cursor, ChatGPT, etc.) as part of the daily workflow not just for one-off snippets

     Comfortable prompting AI tools for tasks like generating dbt models, writing test cases, summarising data quality issues, or drafting documentation

     Applies good judgement about when AI output needs review vs. can be trusted treats AI as a fast first draft, not a final answer

     Curious about applying AI to structural problems: pipeline debugging, anomaly detection, metadata generation, code review support

     Comfortable working in an environment where AI-usage practices are still evolving, and contributes ideas to shape them

NICE TO HAVE

     Experience with data quality tooling (SODA,Collibra, or similar)

     Exposure to cloud platforms (Azure, AWS, or GCP)

     Experience in a regulated or enterprise-scale data environment

     Prior experience mentoring or leading a small pod of engineers

EXPERIENCE

     8+ years in data engineering, with at least 4+ years focused on Snowflake and modern ELT tooling (dbt)

Track record of delivering production-grade pipelines at scale