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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Manager, Enterprise Data - **Company:** AAA Club Alliance Inc. - **Location:** Wilmington, DE, United States - **Experience:** Experienced - **Salary:** $105,147.0 - $167,018.0 - **Contract:** Temporary contract - **Skills:** Sql Data Warehouse, Application Programming Interfaces (APIs), Agile Methodology, Artificial Intelligence, Amazon Web Services, Data Analysis, JIRA, Automation of Tests, Microsoft Azure, BigQuery, Cloud Computing, Information Systems, Databases, Continuous Delivery, Continuous Integration, Data Architecture, Data Discovery, Information Engineering, Data Governance, Data Integration, Extract Transform Load (ETL), Data Mapping, Data Systems, Data Warehousing, Database Design, Key Management, Metadata, Operational Data Store, Operational Databases, Performance Tuning, Reference Data, E2e Testing, Power BI, Software Deployment, SQL Databases, Tableau (Software), Test Data, Strategies of Testing, Workflow Management Systems, Enterprise Data Management, Google Cloud, Cloud Platform System, Technical Debt, Git, Data Layers, Information Technology, Real Time Data, Data Management, Machine Learning Operations, Cloud Migration, Semantic Modeling, Looker Analytics, Software Version Control, Data Pipelines, Devsecops - **Published:** October 21, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=092e929864324eb1 ## About the Role * Bachelor's degree in Computer Science, Information Technology, Data Engineering, Information Systems, Business, or a related field required; an equivalent combination of education and relevant experience may be considered. * 8+ years of progressive experience in enterprise data warehousing, data engineering, analytics, architecture, data management, or governance, including ownership of production data platforms. * 5+ years of experience leading complex data or technology delivery, including roadmap, backlog, financial, vendor, risk, and stakeholder management responsibilities. * 3+ years of people-leadership experience managing multidisciplinary teams that include data architects/modelers, data engineers, analysts, QA/test resources, BI professionals, and external delivery partners. * Demonstrated experience designing and operating cloud data warehouse or lakehouse platforms using services in Google Cloud Platform, Microsoft Azure, or Amazon Web Services; direct experience with BigQuery and related GCP data services is strongly preferred. * Demonstrated experience with conceptual, logical, physical, dimensional, and semantic modeling; source-to-target mapping; slowly changing dimensions; historization; metadata; lineage; and performance-oriented database design. * Strong knowledge of ETL/ELT engineering, SQL, APIs, files, event/stream integration, workflow orchestration, dependency management, error handling, reconciliation, performance tuning, and production support. * Experience implementing CI/CD and DevSecOps practices for data pipelines and infrastructure, including Git-based version control, automated testing, environment promotion, infrastructure as code, release controls, and rollback. * Experience establishing automated data-quality controls, observability, monitoring, alerting, operational SLAs/SLOs, root-cause analysis, incident/problem management, and post-production control frameworks. * Experience leading end-to-end testing and UAT for integrated data solutions, including test strategy, representative data, traceability, regression, reconciliation, defect management, and business sign-off. * Experience managing technology budgets, cloud costs, forecasts, contracts, vendors, and investment cases; working knowledge of FinOps principles and cloud capacity/cost optimization. * Experience with Agile delivery methods such as Scrum and Kanban, backlog and roadmap management, and tools such as Jira or Azure DevOps. * Experience with BI and visualization platforms such as Microsoft Power BI, Tableau, or Looker, including semantic models, certified datasets, security, and capacity governance. * Experience managing multiple stakeholders and delivery partners in a collaborative, fast-paced enterprise environment. Experience with AI/ML data enablement, MLOps, customer identity, master data management, and real-time data products is a plus. * Knowledge Skills and Abilities: * Ability to translate business strategy, operational processes, analytical questions, and control requirements into an integrated data architecture, prioritized roadmap, executable backlog, and measurable delivery plan. * Deep understanding of enterprise data architecture, data warehousing, lakehouse patterns, data modeling, ETL/ELT, orchestration, metadata, lineage, governance, semantic layers, data quality, testing, CI/CD, observability, security, and production operations. * Ability to make sound architecture and investment trade-offs based on business value, data trust, scalability, performance, resiliency, security, maintainability, delivery speed, operating complexity, and total cost of ownership. * Ability to lead through influence across business units, enterprise architecture, infrastructure/cloud, cybersecurity, applications, integration, finance, procurement, BI, data science, and vendor teams. * Ability to establish practical operating mechanisms, standards, controls, decision rights, metrics, review cadences, and accountability that improve delivery quality rather than add unnecessary process. * Strong financial, commercial, and vendor-management acumen, including budgeting, forecasting, capacity planning, contract performance, cloud-cost optimization, and investment justification. * Strong program and project leadership skills, including prioritization, dependency management, resource planning, risk mitigation, executive reporting, conflict resolution, and delivery recovery. * Excellent written, oral, presentation, and facilitation skills, with the ability to communicate technical architecture, delivery risk, cost, quality, and business implications to both executive and technical audiences. * Ability to develop people, set expectations, provide candid feedback, build technical depth, retain institutional knowledge, and create a culture of quality, ownership, and continuous improvement. ## Description * Lead all aspects of the Enterprise Data team, including employees, consultants, managed-service partners, and cross-functional contributors across business analysis, data analysis, architecture, modeling, engineering, quality assurance, BI enablement, platform operations, and production support. * Own the enterprise data warehouse as a technology product and business capability, with accountability for service reliability, delivery performance, stakeholder outcomes, platform health, technical debt, security, cost, and continuous improvement. * Define, maintain, and execute the long-term enterprise data warehouse strategy and multi-year roadmap, including target-state architecture, platform modernization, cloud migration, technical debt reduction, data-product evolution, semantic-layer development, resiliency, and lifecycle management. * Own the consolidated backlog for strategic initiatives, regulatory and control needs, production defects, maintenance, technical debt, platform upgrades, and business enhancements; establish transparent intake, sizing, prioritization, dependency management, release planning, and roadmap communications. * Partner with business leaders, product owners, BI teams, and subject-matter experts to gather and validate business processes, decisions, metrics, events, entities, grain, history, security, service-level, and nonfunctional requirements, translating them into testable epics, features, stories, acceptance criteria, and data contracts. * Lead source-data discovery, profiling, and analysis across databases, APIs, files, streams, and third-party data; ensure teams document source meaning, record behavior, change patterns, authoritative ownership, data quality, lineage, retention, and fitness for use. * Establish and govern enterprise data architecture across ingestion, landing, lake/lakehouse, integrated warehouse, operational data, master and reference data, semantic, presentation, analytics, AI, metadata, security, observability, and consumption layers. * Lead conceptual, logical, physical, dimensional, relational, and semantic data modeling; ensure consistent business definitions, conformed dimensions, appropriate grain and history, reusable structures, lineage, auditability, performance, and alignment to enterprise architecture standards. * Provide technical leadership for ETL/ELT and data integration design and development, including ingestion, cleansing, transformation, source-to-target mapping, business-rule implementation, reconciliation, metadata capture, error handling, restartability, performance tuning, and maintainable code patterns. * Own overall integration job orchestration and dependency design across batch, event-driven, API, streaming, and near-real-time workloads; require dependency-based triggers, controlled parallelism, recovery procedures, failure isolation, alerting, and end-to-end schedule optimization rather than fragile time-based scheduling. * Establish software-engineering and DevSecOps standards for data solutions, including version control, peer review, branching, automated unit and data tests, artifact management, infrastructure as code, environment configuration, secrets management, CI/CD pipelines, segregation of duties, release approvals, rollback, and production deployment. * Define and enforce an end-to-end test strategy covering unit, component, schema, transformation, integration, regression, performance, security, reconciliation, disaster-recovery, and user-acceptance testing; ensure traceability from requirements and source-to-target rules through test evidence and release approval. * Coordinate UAT with business owners and BI consumers, including representative test data, expected results, defect triage, sign-off, release readiness, and validation that downstream reporting and analytics are materially complete before production deployment. * Own the post-production data quality control framework, including preventive and detective controls, file and schema validation, record-count and financial reconciliation, business-rule thresholds, anomaly detection, exception workflows, issue ownership, root-cause analysis, remediation, certification, and control evidence. * Create and maintain production monitoring, alerting, observability, runbooks, support procedures, service measures, and operational dashboards covering availability, freshness, completeness, accuracy, pipeline reliability, latency, recoverability, and cost; ensure recurring incidents and temporary fixes are converted into backlog items and permanent improvements. * Own financial management for the data warehouse environment, including annual operating and capital plans, project forecasts, vendor and license spend, cloud consumption, labor mix, chargeback/showback inputs, business-case development, variance management, and investment optimization. * Drive cloud infrastructure and FinOps optimization across compute, storage, query processing, orchestration, data movement, reservations/commitments, retention, and BI capacity; establish cost transparency, budgets, alerts, unit-cost measures, rightsizing, workload tuning, and architecture trade-off reviews. * Provide accountable project and program delivery for enterprise data initiatives, including scope, schedule, budget, staffing, dependencies, risks, issues, quality, vendor performance, executive reporting, change control, adoption, and benefits realization. * Balance strategic delivery, maintenance, production support, governance, and platform modernization through capacity planning and resource allocation; proactively identify skill, staffing, partner, and knowledge-retention risks. * Lead enterprise data governance execution, including business glossary, data ownership and stewardship, critical data elements, metadata, lineage, access, privacy, retention, master/reference data, quality standards, certification, and controlled change management. * Manage BI platform administration and future direction in coordination with BI leadership, including semantic-model standards, certified datasets, workspace and capacity governance, self-service enablement, security, lifecycle management, and alignment with the enterprise data architecture. * Establish architecture, engineering, quality, and delivery review forums; define measurable standards and quality gates; and ensure exceptions, risks, and technical decisions are documented, approved, and actively managed. * Coach and develop team members; set clear technical and delivery expectations; build succession and skill-development plans; and promote strong business-domain knowledge, engineering discipline, documentation, collaboration, and accountability. Other duties as assigned. *