Sr Data Engineer BI

Quality Bicycle Products
Bloomington, United States of America
2 days ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English
Experience level
Senior

Job location

Bloomington, United States of America

Tech stack

Clean Code Principles
Artificial Intelligence
Amazon Web Services (AWS)
Data analysis
Automation of Tests
Azure
Business Intelligence
Cloud Computing
Code Review
Computer Security
Information Systems
Computer Programming
Continuous Integration
Data Architecture
Information Engineering
Data Governance
Data Infrastructure
Data Integration
ETL
Data Warehousing
Database Queries
Digital Assets
Dimensional Modeling
Python
Machine Learning
Microsoft SQL Server
Cisco Nexus Switches
Operational Databases
Performance Tuning
Scrum
Power BI
TensorFlow
DataOps
Azure
Anaplan
SAP Applications
SAP NetWeaver Business Warehouse
SAP NetWeaver Data Management
SQL Stored Procedures
PL-SQL
SQL Databases
Data Streaming
Systems Integration
T-SQL
Azure
Warehouse Management Systems
Microsoft Power Automate
Azure
PyTorch
Large Language Models
Prompt Engineering
Change Data Capture
Microsoft Fabric
PySpark
Scikit Learn
Information Technology
Performance Monitor
SAP S/4HANA
Real Time Data
Kafka
Spark Streaming
Data Management
Machine Learning Operations
Cloud Integration
Stream Analytics
Software Version Control
Data Pipelines
Databricks

Job description

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.

Technical Leadership & Architecture [25%]

  • 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.

Data Pipeline Development & ETL/ELT [20%]

  • Design, build, and optimize scalable, resilient, and idempotent [LP1.1][SB1.2][LP1.3]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.

AI/ML Development & Agentic Workflows [15%]

  • 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.

Innovation & Emerging Technology [10%]

  • 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.

Data Governance, Quality & Reliability [10%][LP2.1][SB2.2][LP2.3]

  • 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.

Mentorship & Cross-Functional Collaboration [10%]

  • 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.

Operational Support & Production Stewardship [10%]

  • 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.

  • Represents essential duties. Other tasks and responsibilities as assigned., As a senior technical leader, believe in and serve as a role model for Q's DEI mission by creating a work environment where everyone has respect, space, a voice, and can thrive.

Requirements

This is a Hybrid role that is based in the Bloomington, MN Metro area. Applicants must be authorized to work for any employer in the U.S. We are unable to sponsor or take over sponsorship of an employment visa at this time., * 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., * 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.

OTHER RELATED CRITERIA:

Physical Requirements

  • Ability to perform work on a phone and computer extensively.

Model QBP Core Values

  • Act with integrity
  • Be a true partner
  • Create something special
  • Deliver greatness
  • Keep the customer first, * Leader: Inspires teammates to follow them
  • Detail Oriented: Capable of carrying out a given task with all details necessary to get the task done well
  • Innovative: Consistently introduces new ideas and demonstrates original thinking
  • Enthusiastic: Shows intense and eager enjoyment and interest
  • Dedicated: Devoted to a task or purpose with loyalty or integrity, * Flexibility: Inspired to perform well when granted the ability to set your own schedule and goals
  • Growth Opportunities: Inspired to perform well by the chance to take on more responsibility
  • Ability to Make an Impact: Inspired to perform well by the ability to contribute to the success of a project or the organization

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