Devops engineer • milton on
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Rebuild entire pipeline in Power Query (M language) on Power BI Fabric (gen2 dataflows, lakehouses)
Implement resilient source switching (seamless failover if semi-automated exports fail)
Simplify and document logic for non-technical maintainers
Parallel development (current solution remains operational during migration)
Core Tasks
Reverse-engineer legacy Access macros, VBA, SQL, Python to extract business logic
Design and build Power Query dataflows replicating/improving existing transformations
Implement data quality validation ensuring migrated solution matches legacy outputs
Configure multi-source ingestion with upstream failure handling
Document transformations (data dictionaries, process maps, logic documentation)
What you must have:
SQL (Intermediate): Write/optimize joins, aggregations, subqueries, CTEs; reverse-engineer complex queries; translate SQL logic to Power Query.
Python (Intermediate): Read/understand pandas data manipulation scripts; interpret existing Python ETL workflows. Does not need to write production Python.
Power Query (M Language - Foundational): Minimum awareness with willingness to learn quickly under guidance. Strong SQL background enables effective on-the-job learning. Must commit to building exclusively in Power Query (no SQL/Python/Access) for long-term maintainability.
Data Modeling (Strong Dimensional Modeling): Deep star schema understanding; experience with slowly changing dimensions, bridge tables, role-playing dimensions; design models optimized for Power BI/DAX.
Power BI Fabric (Required): Hands-on experience with Fabric workspaces, gen2 dataflows, lakehouses. Understanding Fabric-specific features and differences from gen1 environments. Non-negotiable for 2-month delivery timeline.
Microsoft Access (Reverse-Engineering): Navigate/interpret Access databases (tables, queries, macros); understand Access design patterns; extract business logic for migration (no need to build new Access solutions).
SharePoint (Data Storage/File-Based ETL): Experience using SharePoint as analytics data layer; comfortable with file-based workflows (Excel export ingestion, file path dependencies, refresh schedules).
Data Quality & Documentation: Strong validation/reconciliation instincts; commitment to clear, accessible documentation for non-technical audiences.
Salary/Rate Range: $100 to $120/hr T4
Thank you for your interest in this opportunity. If you are selected to move forward in the process, we will contact you directly. If you do not hear from us, we encourage you to continue visiting our website for other roles that may be a good fit.
