Senior Data Engineer

What is the purpose of the Senior Data Engineer's role?

The Senior Data Engineer’s purpose is to lead Snapper’s data approach in an architectural way, building an asset that scales and leading the team with deep experience in large-scale data warehouses, from the ground up.

This is an experience-based role in our data team: the role is a expert contributor to raising the capability of our existing data engineers as much as it is involved in direct delivery. As a technical leader and role model for the data team, this role will influence and coach junior developers through to experienced peers.

Reports to: Principal Engineer

Direct reports: Up to six

Key relationships: Principal Engineer, the data, engineering and product teams.

Based in: Wellington, NZ

What does the Senior Data Engineer do?

• Leading the technical evaluation of Mosaiq’s user-facing data model and warehouse platform (Redshift, Snowflake or ClickHouse), working directly with our Principal Engineer
• Giving a grounded, experience-based recommendation on warehouse choice, drawn from having led a comparable build or migration before
• Leading execution of the resulting data warehouse build and/or migration, end to end
• Architecting and building scalable data pipelines using Python/PySpark
• Deepening our use of AWS data services (Step Functions, Glue, Lambda, Kinesis, Athena, EMR) to match Mosaiq’s stack
• Acting as a technical leader for the data engineering team — raising the capability of existing, experienced engineers, not only mentoring juniors
• Owning data pipeline testing and data quality practices
• Applying infrastructure-as-code (Terraform) and Git-based CI/CD to the data platform
• Partnering with product and business stakeholders to translate requirements into data architecture decisions
• Bringing outside pattern-recognition from previous large-scale builds into how Snapper approaches data problems

What will help you to be successful in this role? ​

This is an experienced role, you will have stories, lessons and tips and tricks to share from having done it before.

• First-hand experience with large-scale data warehouses, build/migrations (Redshift, Snowflake or ClickHouse) and large data sets
• Proven expertise at data modelling, data storage and partitioning, & SQL queries to support efficient data access
• Demonstrable skills in building resilient and flexible ETL pipelines
• Deep, hands-on AWS data services experience (Step Functions, Glue, Lambda, Kinesis, Athena)
• Strong Python and SQL, with hands-on ELT/ETL pipeline design
• Experience with a modern transformation/orchestration stack (dbt, Airflow, Step Functions or MWAA)

• Infrastructure-as-code (Terraform) and Git-based CI/CD
• Exposure to ML systems and data science would be beneficial
• A track record of raising the capability of experienced peers, not only junior engineers — with concrete examples to draw on

You will likely have between 5–8+ years’ experience, though scope (architecture ownership, migration leadership, mentoring impact) matters more to us than years alone.

To fit with the team, your communication skills will be paramount; you’ll be able to lay out arguments in a well-structured, data-informed, written narrative, and to autonomously query and extract insights from the available data.

You’ll know when a technical solution or an emerging trend can be relevant to the challenges at hand, and have firm beliefs held loosely in an open, collaborative mindset.

We want you to do well, and it looks like this: ​

You’ve done this before, and you know what actually breaks. You bring real, firsthand experience of standing up or migrating a large-scale data warehouse — not textbook familiarity with the tradeoffs. You can walk through what broke, why, and what you’d do differently, and you use that experience to give Paul a grounded, credible recommendation on Mosaiq’s data model and warehouse platform.

You multiply your experience through others. The value of this hire compounds through the team, not just your own output. You help experienced data engineers get better — not only newer ones — through concrete, specific feedback and technical leadership, not general mentoring philosophy.

You build for scale, accuracy and uptime. You architect and build data pipelines that are stable, performant, and well-tested. You value data accuracy and pipeline reliability as much as delivery speed, and you bring rigour to data quality and testing practices.

You see how the pieces connect. You’re curious about how our data model connects to real-world transit data, APIs, and systems across public transport networks, and you help shape architecture decisions with that wider system in mind.

You communicate with clarity, especially on tradeoffs. You can see through vendor framing and marketing claims about platform choice, and communicate the real tradeoffs clearly to both technical and non-technical stakeholders. You raise flags early when something changes.

You grow, and help others grow. You regularly seek feedback and offer it with respect and clarity. You take ownership of your own journey, learn from mistakes, and share your knowledge openly.

You see the power of being in it together. Your communication is well thought out and clear — and works across the team and time zones. You respect deadlines but raise flags early when things change. You default to trusting others and show up in a way that earns that trust back.