AAA Club Alliance is hiring for an Enterprise Data Manager to join our team!
Primary Responsibilities:
- 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.
Minimum Qualifications:
- 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.
Full time Associatesare offered a comprehensive benefits package that includes:
- Medical, Dental, and Vision plan options
- Up to 2 weeks Paid parental leave
- 401k plan with company match up to 7%
- 2+ weeks of PTO within your first year
- Paid company holidays
- Company provided volunteer opportunities + 1 volunteer day per year
- Free AAA Membership
- Continual learning reimbursement up to $5,250 per year
- And MORE! Check out our Benefits Pagefor more information
