Job Description :

- 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).