Data Engineer | Financial Services | MSBI to Azure & ADF
Posted Updated 2
views
Summary
Data engineer at a Sydney financial services organisation, migrating a legacy SQL Server data warehouse to Azure SQL Managed Instance using Azure Data Factory. Day to day: building ETL/ELT and orchestration pipelines, dimensional models, reconciliation and DataOps practices for regulatory and analytical reporting.
Data Engineer | Financial Services | MSBI to Azure & ADF
The role
Join a financial services organisation modernising its enterprise data platform. You’ll help migrate a legacy SQL Server data warehouse to Azure SQL Managed Instance, building secure, tested and well-monitored data solutions that the business can rely on.
What you’ll do
- Design, build, test and support SQL, ETL/ELT and orchestration pipelines
- Deliver migration work end to end: source-to-target mapping, scripting, reconciliation, cutover and handover to support
- Build relational and dimensional models and curated datasets for regulatory, management and analytical reporting
- Create reusable patterns for ingestion, validation, logging, alerting and reconciliation
- Apply DataOps discipline: source control, peer review, testing, deployment controls and rollback planning
- Embed data quality, lineage and control evidence into delivery, with clear runbooks and documentation
What you’ll bring
- 5+ years in data engineering, BI, data warehousing or analytics delivery
- Advanced SQL Server skills: stored procedures, performance tuning and data modelling
- Azure SQL Managed Instance, Azure Data Factory or SSIS Integration Runtime
- Proven experience building, scheduling, reconciling and supporting ETL/ELT pipelines for material data flows
- Strong grounding in dimensional modelling, data quality controls and reconciliation
- Working knowledge of incident, problem and change management
- Clear communicator across technical, business and risk stakeholders
Nice to have
- Banking or financial services background
- Data warehouse or cloud migration experience
- Power BI, Fabric, Databricks, Purview, CI/CD, Python or PySpark
Why apply
Hands-on work on a major warehouse migration, exposure to governance, architecture, risk and reporting teams, and real scope to lift engineering and automation practices.