{"id":1636781,"url":"https://alion.io/job/quality-bicycle-products-sr-data-engineer-bi","title":"Sr Data Engineer BI","company":{"id":1849991,"name":"Quality Bicycle Products","domain":"qbp.com","url":"https://alion.io/company/qbp","size_band":"501-1000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"UltiPro","truth_index":{"grade":"B","score":75,"open_postings":4,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-13T05:45:00Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bloomington, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":120000,"max":130000,"currency":"USD","period":"year","gross":null,"usd_annual":130000},"salary_estimate":null,"experience_years_min":8,"visa_sponsorship":true,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Anomaly Detection","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Azure Data Factory","optional":false},{"name":"Copilot","optional":false},{"name":"Copilot Studio","optional":false},{"name":"Databricks","optional":false},{"name":"Dimensional Modeling","optional":false},{"name":"ETL/ELT","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Master Data Management","optional":false},{"name":"Microsoft Fabric","optional":false},{"name":"MS SQL","optional":false},{"name":"Power BI","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false},{"name":"ABAP","optional":true},{"name":"Agile","optional":true},{"name":"Apache Kafka","optional":true},{"name":"CDS Views","optional":true},{"name":"CI/CD","optional":true},{"name":"Function Calling","optional":true},{"name":"Prompt Engineering","optional":true},{"name":"PyTorch","optional":true},{"name":"RAG","optional":true},{"name":"Scikit-learn","optional":true},{"name":"Scrum","optional":true},{"name":"Spark","optional":true},{"name":"TensorFlow","optional":true},{"name":"Tool Use","optional":true}],"status":"live","first_seen_at":"2026-07-07T18:54:08Z","employer_posted_date":"2026-07-07","last_verified_at":"2026-10-13T17:41:21Z","board_verified":true,"closed_at":null,"days_open":98,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":97},"description":"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.\nWHAT YOU WILL BE ACCOUNTABLE FOR:\nThe 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.\nTechnical Leadership & Architecture [25%]\nOwn the end-to-end data architecture for key BI domains, including ingestion, storage, transformation, semantic modeling, and serving layers.\nLead 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.\nSet and enforce data engineering standards including coding practices, version control, code reviews, and automated testing.\nServe as the technical escalation point for complex data engineering challenges across the BI team.\nData Pipeline Development & ETL/ELT [20%]\nDesign, 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.\nLead the migration of legacy ETL workloads (SAP Data Services, AWS SQL Server) into modern Azure/Fabric patterns as part of the S/4HANA transformation.\nImplement change-data-capture (CDC), incremental loads, retry-safe backfills, and data quality checks across pipelines.\nIntegrate data from SAP S/4HANA, HighJump WMS, Anaplan, Sales Cloud, ShipERP, and external partner feeds into the analytics platform.\nAI/ML Development & Agentic Workflows [15%]\nDesign, develop, train, and deploy machine learning models to support forecasting, anomaly detection, classification, and recommendation use cases across QBP's business domains.\nBuild, 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.\nOperationalize ML models and AI agents through MLOps practices - model versioning, monitoring, drift detection, and automated retraining pipelines.\nPartner with business stakeholders to identify high-value AI/ML opportunities and translate them into production-grade solutions.\nChampion responsible AI practices, ensuring solutions are explainable, secure, and aligned with QBP's data governance standards.\nInnovation & Emerging Technology [10%]\nChampion 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).\nLead Proof-of-Concepts (POCs) to validate new tools and patterns (e.g., Fabric Lakehouse POC, agentic AI workflows), with clear milestones and documentation.\nStay current on industry trends in DataOps, data mesh, lakehouse architectures, and AI-augmented data engineering.\nIdentify and pilot AI/Copilot capabilities in Power BI and Fabric to accelerate analytics delivery.\nData Governance, Quality & Reliability [10%][LP2.1][SB2.2][LP2.3]\nImplement data quality, lineage, cataloging, and master data management practices.\nImplement monitoring, observability, and freshness for critical data pipelines and datasets.\nPartner with InfoSec to implement role-based access, row-level security, and sensitivity labeling for data assets.\nPerform proactive monitoring and root-cause analysis for refresh and ETL failures.\nMentorship & Cross-Functional Collaboration [10%]\nMentor and coach data engineers and BI developers; provide technical guidance, code reviews, and design feedback.\nCollaborate with the SAP, WMS, eCommerce, Security, and Cloud Infrastructure teams to align on timelines, dependencies, and architectural direction.\nPartner with business stakeholders to translate analytics and AI requirements into scalable data solutions.\nOperational Support & Production Stewardship [10%]\nProvide expert-level support for production data pipelines, dataset refreshes, and BI platform stability.\nLead incident response and root-cause analysis for high-severity BI issues.\nDrive continuous improvement in deployment, documentation, and code review standards.\n*Represents essential duties. Other tasks and responsibilities as assigned.\nWHAT YOU NEED TO SUCCEED:\nREQUIRED QUALIFICATIONS:\nBachelor's degree in Computer Science, Information Systems, Engineering, or a related field.\n8+ years of progressive experience in data engineering, business intelligence, or analytics platform development.\nExpert-level SQL (T-SQL, PL/SQL): complex queries, stored procedures, window functions, partitioning, performance tuning.\n5+ years designing and building ETL/ELT pipelines with tools such as Azure Data Factory, SAP Data Services, Fabric Data Pipelines, or equivalent.\nStrong understanding of dimensional modeling (star/snowflake schemas, slowly-changing dimensions) and modern lakehouse / medallion architecture patterns.\nProven experience integrating BI/data platforms with enterprise COTS systems - SAP (ECC, S/4HANA, or BW), HighJump WMS, Anaplan, or equivalent.\nExperience with Power BI semantic modeling and DAX.\nDemonstrated experience leading technical design, mentoring engineers, and driving best practices.\nExcellent communication skills with the ability to translate complex technical concepts for both technical and non-technical stakeholders.\nPREFERRED QUALIFICATIONS:\nDegree in Data Science, Analytics, Computer Science, or related field.\n3+ years programming experience in Python and/or PySpark for data engineering and ML workloads.\nHands-on experience building and deploying machine learning models (scikit-learn, TensorFlow, PyTorch, Azure ML, or Fabric Data Science).\nExperience designing and automating AI agents and agentic workflows using frameworks such as Microsoft Copilot Studio, Azure AI Foundry, or equivalent.\nFamiliarity with LLM integration patterns (RAG, function/tool calling, prompt engineering) and vector databases.\nExperience with MLOps practices and tooling.\nExperience with ETL tools (CDS Views, BDC, DataSphere, Theobald Xtract Universal, or dab Nexus).\nExperience with streaming/real-time data (Kafka, Event Hubs, Fabric Real-Time Intelligence, or Spark Structured Streaming).\nExperience with DataOps / CI-CD for data.\nFamiliarity with Microsoft Purview or other data governance/cataloging platforms.\nWorking knowledge of AWS/Fabric for cross-cloud integration.\nExperience with Agile/Scrum methodologies.\nOTHER RELATED CRITERIA:\nPhysical Requirements\nAbility to perform work on a phone and computer extensively.\nModel QBP Core Values\nAct with integrity\nBe a true partner\nCreate something special\nDeliver greatness\nKeep the customer first\nAs 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.\nQuality Bicycle Products is a proud certified B-Corp and an Equal Employment Opportunity employer committed to diversity, equity, and inclusion. We welcome talent from all backgrounds and encourage employees to bring their authentic selves to work. We do not discriminate based on race, color, religion, national origin, sex (including pregnancy and related conditions), sexual orientation, gender identity or expression, age, veteran status, disability, genetic information, political views or activity, or any other protected characteristic.\nAt Quality Bicycle Products, we approach pay ethically and transparently. Our pay ranges are informed by third-party market data and aligned with internal equity across similar roles. 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