{"id":4044119,"url":"https://alion.io/job/apgfcu-data-engineer","title":"Data Engineer","company":{"id":2252172,"name":"APGFCU","domain":"apgfcu.com","url":"https://alion.io/company/apgfcu","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":75,"open_postings":3,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-11-10T06:52:35Z"}},"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":"explicit","locations":["Edgewood, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":91600,"max":155600,"currency":"USD","period":"year","gross":null,"usd_annual":155600},"salary_estimate":null,"experience_years_min":7,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Azure","optional":false},{"name":"Azure Data Factory","optional":false},{"name":"Azure DevOps","optional":false},{"name":"Azure SQL Database","optional":false},{"name":"Dimensional Modeling","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Microsoft Fabric","optional":false},{"name":"MS SQL","optional":false},{"name":"Power BI","optional":false},{"name":"SQL","optional":false},{"name":"Terraform","optional":false}],"status":"live","first_seen_at":"2026-11-10T23:14:43Z","employer_posted_date":"2026-11-10","last_verified_at":"2026-11-10T23:14:43Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"Where Banking Feels Like Belonging\nAt APGFCU, our values are at the heart of everything we do, empowering us as leaders in the credit union movement. We prioritize purpose and connection in how we serve our members, co-workers and communities - helping to navigate life's little and big moments.\nWho we are:\nAs a member-owned, not-for-profit credit union, APG Federal Credit Union (APGFCU®) empowers our members with financial products and services to support them at every stage of their financial lives, helping them become money smart and financially independent. We proudly serve people in:\nHarford County\nCecil County\nBaltimore County\nBaltimore City\nWe believe banking should feel personal, supportive, and human. That goes for our members and our employees. Here, you’re not just filling a role - you’re making a difference in your community.\nOur team lifts each other up and our leaders invest in your growth. We celebrate wins, recognize and value contributions, and support each other through challenges. We strive for meaningful connections across our teams, because that’s what belonging is all about.\nLet’s build something meaningful together.\nRole Summary:\nThe Data Engineer is responsible for designing, building, and maintaining the data architecture, pipelines, and integration frameworks which enable reliable, secure, and timely access to enterprise data across cloud-based and on-premises data sources. This role establishes the foundation for business intelligence, advanced analytics, artificial intelligence, and data-driven decision-making across the organization.\nWorking within the Emerging Technologies Group, the Data Engineer partners with technology teams, business stakeholders, data analysts, third-party vendors, and other subject matter experts to transform data from disparate operational systems into trusted, accessible, and actionable information.\nThe ideal candidate combines strong software engineering and data management skills with practical experience in the Microsoft data ecosystem. Success in this role requires an ownership mindset, attention to data integrity, and an understanding of how well-engineered data platforms enable improved operational efficiency, financial oversight, member experiences, and future innovation.\nKey Responsibilities:\nData Architecture, Storage, and Modeling (25%):\nBuild and maintain scalable cloud data platforms, including data lakes, data warehouses, and lakehouse architectures.\nDevelop and maintain data models that support operational reporting, financial analysis, enterprise analytics, and self-service business intelligence. \nApply dimensional modeling, relational modeling, and other appropriate data design principles to meet business and analytical requirements. \nPromote consistent data definitions, structures, transformation logic, and integration standards across data products and platforms. \nDesign data solutions that balance performance, scalability, maintainability, security, and cost. \nPartner with technology leadership to identify opportunities to modernize the data estate and align engineering investments with business priorities. \nSupport the development of a well-structured data foundation that can accommodate future analytics, automation, and AI initiatives. \nData Engineering and Pipeline Development (25%):\nDesign, develop, deploy, and maintain reliable, scalable data pipelines that integrate data from internal applications, databases, cloud platforms, APIs, and third-party providers. \nBuild and optimize ETL/ELT processes using Microsoft Azure data services, including Azure Data Factory, Azure Synapse Analytics, and Microsoft Fabric, as appropriate to the organization's architecture. \nDevelop reusable data ingestion and transformation frameworks for structured, semi-structured, and unstructured data. \nImplement full and incremental data loading, Change Data Capture (CDC), historical data preservation, and other appropriate data movement patterns. \nDevelop batch and where business requirements warrant, event-driven and near-real-time data processing capabilities. \nIntegrate data from core financial systems, lending and deposit applications, digital banking platforms, CRM systems, and other enterprise sources. \nEstablish reusable integration patterns, coding standards, and engineering practices that improve maintainability, consistency, and delivery speed. \nPerformance, Reliability, and Operational Support (20%):\nMonitor data pipelines, scheduled jobs, integrations, and supporting infrastructure to maintain reliability and meet established service-level expectations. \nIdentify and resolve bottlenecks affecting query performance, pipeline execution, resource utilization, data availability, and processing costs. \nDevelop alerting, logging, retry mechanisms, and failure recovery procedures to improve operational resilience.\nTroubleshoot production and non-production issues, perform root-cause analysis, and implement corrective and preventive measures. \nImplement automated testing for data ingestion, transformation logic, data quality rules, and pipeline execution.\nParticipate in production deployments, release management, incident resolution, and ongoing platform improvements. \nMaintain appropriate operational procedures, support documentation, and recovery processes. \nMicrosoft Data Platform and Business Intelligence Enablement (15%):\nDevelop and maintain data solutions using the organization's Microsoft technology stack, including Azure SQL, SQL Server, Azure Data Factory, Azure Synapse Analytics, and Microsoft Fabric, as applicable. \nWrite and optimize complex T-SQL queries, stored procedures, and data transformation logic. \nPrepare curated, well-documented datasets for Power BI reporting, semantic models, and enterprise analytics. \nCollaborate with BI developers and business analysts to implement dimensional models and reusable data structures that support consistent reporting and KPI definitions. \nTroubleshoot data discrepancies and work with report developers and business stakeholders to identify root causes. \nSupport integration between enterprise data platforms and approved Microsoft analytics, automation, and AI services. \nEvaluate opportunities to simplify data movement, reduce redundant processing, and improve the usability of trusted data. \nData Quality, Governance, and Financial Controls (15%):\nImplement automated data validation, cleansing, completeness, accuracy, consistency, and integrity checks throughout the data lifecycle. \nDevelop reconciliation processes to verify that data transferred between source systems, integration pipelines, analytical platforms, and downstream consumers is complete and accurate. \nEstablish appropriate interface controls, exception handling, error logging, and monitoring to identify and resolve discrepancies. \nSupport financial data controls, including transaction balancing and general ledger reconciliation, where applicable to the organization's business processes. \nCollaborate with business and technology stakeholders to define data quality expectations, ownership, standards, and remediation procedures. \nMaintain appropriate metadata, technical documentation, data lineage, and transformation logic to support traceability and auditability. \nContribute to data governance practices that promote trusted, consistent, and responsibly managed enterprise data. \nPhysical Demands: Most of your time will be spent working at a computer, collaborating in meetings, and connecting with colleagues and stakeholders. Occasionally, you may attend events, move between workspaces or travel for business. We’re committed to providing reasonable accommodation to support employees in performing the essential responsibilities of the role.\nA Typical Day in the Role:\nBuild and Maintain Pipelines: Develop and improve data ingestion, transformation, and integration processes across enterprise sources.\nModel and Prepare Data: Create reliable data structures and curated datasets for reporting, analytics, and approved AI initiatives \nValidate and Reconcile Data: Investigate data quality issues, implement controls, and verify that information is complete and accurate.\nMonitor Platform Health: Review pipeline runs, scheduled jobs, query performance, alerts, and resource utilization; resolve issues and identify root causes.\nPartner Across Teams: Work with analysts, BI developers, application teams, business stakeholders, vendors, and technology leadership to clarify needs and deliver solutions.\nImprove and Document: Maintain data models, architecture diagrams, lineage, operating procedures, and recommendations for better performance, reliability, and cost efficiency.\nQualifications:\nBachelor’s degree in computer science, Information Systems, Data Engineering, Software Engineering, or a related technical discipline; equivalent relevant experience may be considered. Seven or more years of experience in data engineering and related discipline such as data science, data governance, or data analytics.\nExperience: 7+ years of experience in data engineering or related discipline. Experience within financial services, banking, fintech, or regulated industries is preferred.\nMicrosoft Data Technologies: Practical experience with SQL Server, T-SQL, Azure SQL, Azure Data Factory, Azure Synapse Analytics, and/or Microsoft Fabric.\nData Engineering: Experience building ETL/ELT pipelines, integrating APIs and disparate systems, and working with structured, semi-structured, and unstructured data.\nData Modeling and BI: Knowledge of relational and dimensional modeling, data warehouses, datalakes, lakehouses, and preparing governed datasets for Power BI and analytics.\nProgramming and Delivery: Ability to develop production-quality SQL; familiarity with source control, testing, Azure DevOps, or Terraform.\nQuality, Security, and Controls: Strong attention to data integrity, reconciliation, metadata, lineage, privacy, access controls, and financial-services compliance.\nProblem Solving and Communication: Strong analytical and troubleshooting skills, ownership of deliverables, and ability to explain technical concepts and trade-offs to technical and nontechnical audiences.\nWhat Success Looks Like: \nReliable, Timely Data: Pipelines and integrations deliver trusted data within established service-level expectations.\nImproved Data Quality: Automated validation and reconciliation reduce discrepancies and improve completeness, accuracy, and consistency.\nUseful Analytics Foundations: Well-modeled, documented datasets enable consistent Power BI reporting, semantic models, KPIs, and enterprise analytics.\nSecure and Auditable Operations: Data access, movement, and transformation follow security requirements and support traceability, audits, and financial controls.\nStable and Efficient Platforms: Query performance, pipeline execution, resource use, recovery, and operating costs are continually monitored and improved.\nPredictable Delivery: Engineering work is tested, documented, version-controlled, and deployed through approved practices.\nStrong Partnerships and Ownership: Stakeholders understand progress, risks, and trade-offs; initiatives are owned through implementation and production support.\nHow You Show Up: Core Competencies\nOrganizational Competencies: Agility, Learning and Innovation: Being able to handle change, learn quickly, and adjust how you work as things evolve. It also means sharing ideas that help the organization grow.\nCollaboration and Teaming: Working well with others to reach shared goals, strengthening partnerships across departments, and creating meaningful experiences for members and teammates.\nEmotional Intelligence: Understanding your own emotions and the emotions of others so interactions stay productive and relationships stay strong. It’s balancing logic and feeling to navigate challenges and support a healthy workplace.\nListening and Communication: Communicating clearly, respectfully, and openly. 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