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)