Sr Data Engineer BI

Wheels Manufacturing LLC•Bloomington, MN

18h•$120,000 - $130,000•Hybrid

About The Position

The Senior Data Engineer is a hands-on technical leader responsible for designing, building, and evolving QBP's modern data platform that powers Business Intelligence, AI, and advanced analytics across the enterprise. This role bridges current-state SAP Data Services and SQL Server data warehousing with the future-state Microsoft Fabric / OneLake lakehouse architecture, while integrating data from QBP's complex enterprise landscape including SAP S/4HANA, HighJump WMS, Anaplan, Prophix, Sales Cloud, and eCommerce. Beyond traditional data engineering, this role will lead the development of AI/ML capabilities - building machine learning models, designing and automating AI agents, and operationalizing agentic workflows that augment business decision-making. The Senior Data Engineer is expected not only to deliver, but to innovate evaluating emerging technologies, championing modern data engineering practices, and shaping the architectural direction of QBP's data and AI platform.

Requirements

Bachelor's degree in Computer Science, Information Systems, Engineering, or a related field.
8+ years of progressive experience in data engineering, business intelligence, or analytics platform development.
Expert-level SQL (T-SQL, PL/SQL): complex queries, stored procedures, window functions, partitioning, performance tuning.
5+ years designing and building ETL/ELT pipelines with tools such as Azure Data Factory, SAP Data Services, Fabric Data Pipelines, or equivalent.
Strong understanding of dimensional modeling (star/snowflake schemas, slowly-changing dimensions) and modern lakehouse / medallion architecture patterns.
Proven experience integrating BI/data platforms with enterprise COTS systems - SAP (ECC, S/4HANA, or BW), HighJump WMS, Anaplan, or equivalent.
Experience with Power BI semantic modeling and DAX.
Demonstrated experience leading technical design, mentoring engineers, and driving best practices.
Excellent communication skills with the ability to translate complex technical concepts for both technical and non-technical stakeholders.

Nice To Haves

Degree in Data Science, Analytics, Computer Science, or related field.
3+ years programming experience in Python and/or PySpark for data engineering and ML workloads.
Hands-on experience building and deploying machine learning models (scikit-learn, TensorFlow, PyTorch, Azure ML, or Fabric Data Science).
Experience designing and automating AI agents and agentic workflows using frameworks such as Microsoft Copilot Studio, Azure AI Foundry, or equivalent.
Familiarity with LLM integration patterns (RAG, function/tool calling, prompt engineering) and vector databases.
Experience with MLOps practices and tooling.
Experience with ETL tools (CDS Views, BDC, DataSphere, Theobald Xtract Universal, or dab Nexus).
Experience with streaming/real-time data (Kafka, Event Hubs, Fabric Real-Time Intelligence, or Spark Structured Streaming).
Experience with DataOps / CI-CD for data.
Familiarity with Microsoft Purview or other data governance/cataloging platforms.
Working knowledge of AWS/Fabric for cross-cloud integration.
Experience with Agile/Scrum methodologies.

Responsibilities

Own the end-to-end data architecture for key BI domains, including ingestion, storage, transformation, semantic modeling, and serving layers.
Lead design and implementation of QBP's medallion (Bronze/Silver/Gold) architecture on Microsoft Fabric / OneLake, integrating with the SQL Server data warehouse during the modernization transition.
Set and enforce data engineering standards including coding practices, version control, code reviews, and automated testing.
Serve as the technical escalation point for complex data engineering challenges across the BI team.
Design, build, and optimize scalable, resilient, and idempotent data pipelines using Azure Data Factory, Fabric Data Pipelines, SAP Data Services (current state), Python/PySpark, and SQL.
Lead the migration of legacy ETL workloads (SAP Data Services, AWS SQL Server) into modern Azure/Fabric patterns as part of the S/4HANA transformation.
Implement change-data-capture (CDC), incremental loads, retry-safe backfills, and data quality checks across pipelines.
Integrate data from SAP S/4HANA, HighJump WMS, Anaplan, Sales Cloud, ShipERP, and external partner feeds into the analytics platform.
Design, develop, train, and deploy machine learning models to support forecasting, anomaly detection, classification, and recommendation use cases across QBP's business domains.
Build, configure, and automate AI agents and agentic workflows (e.g., Microsoft Copilot Studio, Azure AI Foundry, LangChain/LangGraph, or equivalent) that integrate with QBP's enterprise systems and data platform.
Operationalize ML models and AI agents through MLOps practices - model versioning, monitoring, drift detection, and automated retraining pipelines.
Partner with business stakeholders to identify high-value AI/ML opportunities and translate them into production-grade solutions.
Champion responsible AI practices, ensuring solutions are explainable, secure, and aligned with QBP's data governance standards.
Champion innovation by evaluating, prototyping, and recommending emerging data and AI technologies (e.g., Microsoft Fabric, SAP BDC/DataSphere, Databricks, Delta Sharing, real-time analytics, LLM-based agents).
Lead Proof-of-Concepts (POCs) to validate new tools and patterns (e.g., Fabric Lakehouse POC, agentic AI workflows), with clear milestones and documentation.
Stay current on industry trends in DataOps, data mesh, lakehouse architectures, and AI-augmented data engineering.
Identify and pilot AI/Copilot capabilities in Power BI and Fabric to accelerate analytics delivery.
Implement data quality, lineage, cataloging, and master data management practices.
Implement monitoring, observability, and freshness for critical data pipelines and datasets.
Partner with InfoSec to implement role-based access, row-level security, and sensitivity labeling for data assets.
Perform proactive monitoring and root-cause analysis for refresh and ETL failures.
Mentor and coach data engineers and BI developers; provide technical guidance, code reviews, and design feedback.
Collaborate with the SAP, WMS, eCommerce, Security, and Cloud Infrastructure teams to align on timelines, dependencies, and architectural direction.
Partner with business stakeholders to translate analytics and AI requirements into scalable data solutions.
Provide expert-level support for production data pipelines, dataset refreshes, and BI platform stability.
Lead incident response and root-cause analysis for high-severity BI issues.
Drive continuous improvement in deployment, documentation, and code review standards.