Hiring at three levels - Senior Data Engineer, Data Engineering Lead and Senior Data Engineering Lead, You'll build the governed data platforms that analytics, machine learning and generative AI are built on.
The role
- Build and optimise batch and streaming pipelines and ETL/ELT frameworks with SQL, Python and Spark
- Deliver data platforms on AWS (Glue, Redshift, Athena, MSK, MWAA, Lake Formation) and/or Databricks (Unity Catalog, Delta Lake)
- Model data for real query patterns across warehouse and lakehouse, including open table formats such as Iceberg
- Build data quality, lineage, access control and FinOps into the platform, with IaC and CI/CD throughout
- At Lead level and above: lead a workstream and grow a team. Senior Leads own the data architecture across a programme and the commercial conversation with stakeholders
- Senior Data Engineer: 3-6 years.
- Lead: 6-10 years.
- Senior Lead: 10-15 years in data engineering, data architecture or technical leadership
- Hands-on AWS and/or Databricks data platform experience, with strong SQL, Python and Spark
- Pipelines you've taken into production and kept running
- At Lead level and above: a track record leading teams and delivering complex data platforms end to end, ideally including time in consulting or professional services
- Financial services or regulated industry experience is preferred
Culture
- A collaborative data and AI engineering community that shares what works
- Mentoring and coaching at every level
- Hybrid in Sydney, with time on site with the team at agreed intervals
- $120k-$240k package, depending on level
- Permanent, or contract at $800-$1,200 per day
- Support to gain AWS, Databricks and other certifications
- A fast process: CVs reviewed within 48 hours and no take-home or live coding test.
