Senior Data Engineer

• 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

• 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

• 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

• 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

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