Curated Data Integration Engineer

Hollstadt Consulting is a management and technology consulting firm dedicated to placing professionals at engagements where they will excel. When you work with us, you'll work with a refreshingly real company led and staffed by seasoned experts who are also down-to-earth, good people. We're committed to treating you with respect and helping you achieve your career aspirations.

Since 1990, Hollstadt has been a trusted partner to more than 150 domestic and global companies and has successfully completed over 3,000 projects. Our continued growth has created challenging and rewarding opportunities for accomplished IT and Business Consultants. Hollstadt Consulting is an equal opportunity employer including disability/veteran.

By applying for this job, you agree to receive calls, AI-generated calls, text messages, or emails from Hollstadt Consulting and its affiliates, and contracted partners. Frequency varies for text messages. Message and data rates may apply. Carriers are not liable for delayed or undelivered messages. You can reply STOP to cancel at any time.

Role:
Senior Data Platform Engineer

Rate: $ 65.34/hour W2

Location:

Minneapolis, MN - 3 days onsite required

The client's Data Analytics Platform (DAP) spans the full data lifecycle on Azure. Batch and file-based sources flow through a multi-zone Delta Lake medallion lakehouse on ADLS Gen2, processed with Azure Synapse Analytics. A real-time tier on Azure Data Explorer handles streaming and sensor telemetry. Azure SQL Database holds orchestration metadata and operational state, master data management provides enterprise golden records, containerized Python jobs on Azure Container Apps handle API extraction, and Logic Apps handle lightweight intake and notification workflows.

Power BI delivers reporting tiered by data velocity: high-velocity real-time dashboards from Azure Data Explorer, near-real-time views from Azure SQL, and analytical reporting from curated serverless SQL marts in the lakehouse. Access is governed by a domain-based classification model aligned to Minnesota government data practices requirements, and all code deploys through Azure Dev Ops CI/CD.

This is a generalist, delivery-focused engineering role built for independent work inside an established platform. The role reports administratively to an IT manager. The client's Data Architect owns the platform's architecture, standards, and conventions and reviews and approves design and code; within those guardrails, the engineer owns how the work gets done. The work is broad: onboarding new data sources, extending and hardening existing pipelines, resolving defects across the estate, building curated data products, and improving the shared framework itself where it falls short.

We are not looking for a narrow specialist. We are looking for a specific working style: an engineer who can take an ambiguous request, investigate the systems involved, read the existing code and metadata before writing anything new, propose an approach, and carry the work to done. Direction comes from the platform's architecture and standards rather than step-by-step instruction: the Data Architect reviews and approves outcomes, and the engineer owns everything in between.

• Treat an unfamiliar system, API, or codebase as something to investigate, not a blocker. You read documentation, trace existing pipelines, test assumptions, and come back with findings and a proposed path, not just questions.
• Work within an established framework and its conventions, and improve it through its own patterns when it falls short, rather than building one-off solutions around it.
• Leave code better than you found it, with the discipline to keep changes surgical: fix what is broken, flag what is fragile, and avoid both unrequested rewrites and shortcuts that leave technical debt behind.
• Communicate status honestly and early, including when something is harder than expected or when you find a problem nobody asked about.

• Source onboarding: Build ingestion for new data sources (REST APIs, SFTP drops, network file shares, vendor exports, sensor and telemetry feeds) through the metadata-driven orchestration framework, landing raw data and promoting it through the medallion zones with PySpark notebooks and Synapse pipelines.
• Curated data products: Develop serverless SQL views and Delta Lake tables in the curated consumption layer that apply business rules and serve analytics, reporting, and downstream feeds, following the platform's schema-per-mart and access-control conventions.
• Extraction jobs: Develop and maintain containerized Python jobs on Azure Container Apps for API extraction workloads, including managed identity authentication, structured logging to the orchestration database, and YAML-based CI/CD deployment.
• Troubleshooting and defect resolution: Diagnose and resolve pipeline failures, data quality issues, and performance problems across the…