Senior Data & Analytics Enterprise Program Lead

Randstad Digital Americas

Newport Beach (CA)

On-site

USD 166,000 - 187,000

Full time

19 hours ago

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Job summary

Randstad Digital Americas is seeking a Senior Technical Program Manager to lead cross-functional data initiatives across enterprise reporting, analytics, AI, governance, and business decision-making. You will collaborate with Data Engineering, Architecture, Analytics, AI, Product, Security, and other teams to deliver multi-year data platform modernization and governance programs.

The initial assignment centers on a remediation program addressing data privacy, retention, and test data management,

Qualifications

10+ years of program and project management experience in complex enterprise environments.
5+ years delivering data, analytics, data platform, or data transformation initiatives.
Proven experience leading initiatives across multiple technology teams, business areas, and external delivery partners.
Experience operating in Agile, product‑focused, SAFe, or hybrid delivery environments.
Demonstrated ownership of program financials, forecasting, capacity planning, governance, and executive reporting.
Working knowledge of enterprise data platforms, cloud data ecosystems, data warehouses, lakehouse concepts, data products, and shared data services.
Experience leading data risk, privacy, retention, records management, test data management, or other regulatory remediation initiatives involving multiple systems and accountable business and technology owners.
3+ years of experience leading or supporting non‑production data protection initiatives, including data masking, tokenization, de‑identification, synthetic data generation, or comparable controls across development, test, and UAT environments.
Experience with data integration and ETL/ELT delivery, migration, source‑to‑target dependencies, reconciliation, and downstream consumption.
Understanding of data governance, stewardship, quality, metadata, lineage, security, privacy, and operational support models.
Ability to connect delivery decisions across the end-to‑end data lifecycle, from upstream sources through platforms and transformation layers to analytical, financial, actuarial, operational, and AI consumers.
Demonstrated ability to work directly and credibly with engineers, architects, product leaders, security teams, and operations teams.
Ability to understand platform, integration, data flow, environment, testing, migration, and operational‑readiness concepts.
Ability to identify technical risks and dependencies, ask informed questions, challenge assumptions, and uncover delivery gaps.
Strong executive presence, written communication, facilitation, and presentation skills.
Proven ability to influence without authority, navigate competing priorities, manage conflict constructively.
Ability to translate technical concepts, trade‑offs, and risks into business outcomes and clear recommendations.
Bachelor's degree in Computer Science, Information Systems, Engineering, Business, or a related field preferred; equivalent experience considered.
PMP, PgMP, Agile, SAFe, or comparable program/project management certification preferred.
SnowPro Core Certification, CDMP, DP‑900 certification preferred.
Strong problem framing, critical thinking, sound judgment, and practical solution development.
Outcome‑oriented ownership with the initiative to move beyond passive coordination and maintain momentum through ambiguity.
Ability to balance strategic context with detailed execution and to adapt quickly as priorities, dependencies, or delivery conditions change.
Strong analytical discipline, risk awareness, continuous improvement orientation, and attention to delivery quality.
Prior experience with a major consulting firm or in a consulting engagement leadership role is a plus.

Responsibilities

Lead complex, multi-year data and analytics initiatives with accountability for end-to-end delivery, business outcomes, and executive visibility.
Drive delivery across enterprise data platform modernization, shared data services, data products, data governance, metadata management, data quality, data integration, migration, reporting and analytics enablement, and AI/ML foundations.
Develop and manage integrated program plans, roadmaps, milestones, dependencies, decisions, readiness criteria, adoption measures, and delivery risks.
Ensure delivery aligns with enterprise data strategy, architecture direction, business priorities, and committed outcomes.
Partner directly with Data Engineering, Architecture, Analytics, Product, Security, and application teams to understand technical concepts, design decisions, delivery models, dependencies, and operational impacts.
Validate delivery sequencing, implementation readiness, migration plans, integration approaches, and downstream consumer impacts.
Engage credibly in discussions involving source ingestion, ETL/ELT pipelines, cloud data platforms, data warehouses and lakehouses, APIs and integrations, data lineage, metadata, data quality, reporting ecosystems, and AI/ML enablement.
Translate technical discussions into actionable plans, clear ownership, decisions, and executive-ready communications.
Operate effectively within Agile, product-focused, SAFe, and hybrid delivery models while maintaining enterprise-level execution discipline.
Own scope, schedule, financials, resources, dependencies, risks, issues, decisions, change control, delivery health, and governance for assigned programs.
Establish clear ownership, drive decisions to closure, challenge readiness assumptions, and proactively address gaps that could affect outcomes.
Move beyond status collection to active problem-solving, escalation, decision facilitation, and removal of technical or organizational friction.
Ensure data governance, stewardship, quality, metadata, lineage, security, privacy, retention and disposition, test data management, regulatory, audit, and operational support considerations are integrated into planning and execution.
Drive clarity on data classification, retention requirements, appropriate use and protection of production data in non-production environments, remediation ownership, control evidence, and long‑term operating accountability.
Partner with data producers and consumers to establish clear internal expectations for data ownership, integration, quality, and consumption.
Drive migration and cutover readiness, validation, reconciliation, adoption, support‑model definition, and sustainable transition into operations.
Maintain a forward‑looking risk view to prevent late discovery of data gaps, unmanaged dependencies, integration issues, or adoption risks.