We are committed to promoting a diverse and inclusive workplace - a place where we can each be ourselves and do the best work of our lives. At Tracksuit we consider a unique perspective, diverse experience and a growth mindset to be of greater value than ticking every box on a JD. If you think you have what it takes but don’t have the exact experience we’ve set out, please still get in touch and apply. We’d love to chat!
At Tracksuit we provide beautiful, radically affordable, always-on brand tracking which is easy to use compared to traditional methods. We recognise that sounds like a ton of buzzwords for marketers, so what it really means is that we did over tens of thousands of surveys last month to find out what real humans think and feel about the world’s best brands and their competitors. Check us out here 🙌
Are you our new Senior Data Engineer?
At Tracksuit, data isn't a back-office function — it's the backbone of every decision we make. In this role, you'll build and evolve the internal analytics platform that powers our Product, Finance, and GTM teams (Customer Success, Sales, and Marketing) across our growing global organisation.
You'll work closely with Data Engineers, Data Analysts, Software Engineers, Product Managers, and Operations colleagues across our international offices, shaping the quality and strategic direction of our internal data capabilities at scale.
👉 Please note: This role can be based in either our Auckland or Sydney office.
As our Senior Data Engineer, you'll specifically be responsible for:
Acting as a lead data authority — owning data quality, reliability, and accuracy across our internal analytics platform, and setting the standard for how we work with data at Tracksuit
Managing, scaling, and continuously enhancing our core Analytics data platform (Snowflake + dbt) as Tracksuit expands globally
Maintaining and enhancing our data integrations with internal and external systems (including HubSpot, Subskribe, Vitally, and our product database), ensuring seamless data flow and accessibility
Designing and building self-service data products that meet complex internal business requirements across Finance, GTM, and Product — reducing reliance on ad-hoc requests and enabling faster decisions
Identifying and delivering high-value analytics capabilities for internal stakeholders, translating business problems into well-modelled, trustworthy data
Mentoring junior team members, establishing robust data engineering practices, and helping shape the future of our growing global data capabilities
That’s the role, so who are you?
5+ years of experience designing, developing, and managing large-scale data platforms, with strong expertise in cloud data warehousing (e.g. Snowflake, Databricks, BigQuery, Redshift)
Advanced SQL and dbt proficiency — you write clean, well-tested transformation logic and understand the difference between a well-organised set of dbt models and a true semantic layer
Strong understanding of data modelling techniques — you know when to apply dimensional modelling, Data Vault, or a simpler normalised approach based on the problem at hand
Experience with data integration pipelines — building and maintaining ELT pipelines from SaaS sources (CRM, billing, product databases) into a centralised warehouse
Infrastructure-as-Code experience — you're comfortable managing data infrastructure programmatically (e.g. Terraform) and understand why it matters at scale
Exceptional collaboration skills — you can translate ambiguous business questions into clear data requirements, communicate confidently with non-technical stakeholders, and contribute positively to a fast-moving, distributed team
A genuine curiosity about how data drives business decisions — you care about the downstream impact of the work, not just the technical elegance of the pipeline
Nice-to-have
Experience with AWS cloud infrastructure (S3, Lambda, Glue)
Experience building or contributing to a semantic layer or metrics layer
Prior experience in a B2B SaaS environment
Qualifications
Bachelor's degree (or higher) in Computer Science, Data Science, Information Technology, or a closely related analytical field — or equivalent practical experience
