Role Summary

The Data Management Lead will oversee the architecture, governance, and operationalization of the Lakehouse platform. This role bridges the gap between complex data engineering and strategic business outcomes. The ideal candidate will take ownership of the entire data lifecycle—from real-time streaming and unified storage to secure AI enablement—ensuring the ecosystem remains scalable, compliant, and highly performant for advanced analytics and enterprise reporting.

Core Responsibilities

Lead the organization's Enterprise Data Management strategy and roadmap.
Design and implement Data Governance frameworks, policies, standards, and operating models.
Establish Data Stewardship and Data Ownership across business domains.
Drive enterprise Data Quality initiatives, including profiling, monitoring, cleansing, and KPI reporting.
Lead Metadata Management, Business Glossary, Data Catalog, and Data Lineage implementation.
Define and govern Master Data Management (MDM) and Reference Data Management practices.
Collaborate with Enterprise Architects to define enterprise data models and information architecture.
Oversee data integration across ERP, CRM, APIs, cloud applications, and operational systems.
Guide the implementation of modern Data Warehouse, Data Lake, and Lakehouse architectures.
Lead vendor evaluations, open-source technology adoption, and continuous platform optimization to reduce latency and infrastructure costs.
Partner with business stakeholders to identify opportunities for data-driven decision-making.
Mentor and lead cross-functional teams, including Data Engineers, Data Analysts, and Data Stewards.

Architecture & Infrastructure Operations

Drive the operational strategy for the data lakehouse, leveraging open table formats like Apache Iceberg and high-performance analytical engines like ClickHouse and Trino.
Oversee the design and health of real-time event streaming and stream-processing pipelines utilizing Kafka and Flink.
Collaborate with DevOps and infrastructure teams to ensure seamless orchestration (e.g., Airflow/dbt) and resilient object storage (MinIO).

Qualifications & Technical Expertise

Experience: 5+ years in data architecture, data engineering, or data management leadership roles building enterprise-scale data platforms.
3+ years in a leadership or team management role.
Strong knowledge of enterprise data governance frameworks and best practices.
Experience delivering enterprise-scale data transformation initiatives.
Excellent stakeholder management and communication skills.
Strong analytical and problem-solving capabilities.
Modern Data Stack: Proven expertise managing environments that utilize Kafka, Flink, Apache Iceberg, and Apache Spark.
Security & Governance: Hands-on experience implementing enterprise security protocols (OIDC, OAuth2) and managing data catalogs.
Cloud & DevOps: Strong understanding of containerized deployments (Docker/Kubernetes), CI/CD pipelines, and scalable object storage.
Leadership: Demonstrated ability to lead cross-functional technical teams, manage stakeholder expectations, and translate business requirements into scalable data models.

Preferred Certifications

Certified Data Management Professional (CDMP)
TOGAF