What are the responsibilities and job description for the Director, Enterprise Data Platform Engineering position at Jobs via Dice?
Job Description
The Director, Enterprise Data Platform Engineering is responsible for leading the build and ongoing operation of a lakehouse-style data infrastructure that enables reliable, scalable, and governed enterprise data products.
This role leads a technical delivery organization, typically composed of managers and senior level individual contributors, and is accountable for platform capabilities spanning data ingestion, data structuring, master data management (MDM), data quality, data security and servicing layer contracts.
This position balances people leadership and delivery accountability. The Director partners across departments and with enterprise architects on cross-functional initiatives to establish ingestion standards, medallion/zone patterns (e.g., raw/curated/serving), metadata management, and operational controls that improve platform maturity, data trust, and time-to-value for downstream consumers.
Responsibilities
Lead and manage the technical development and delivery function supporting the enterprise lakehouse data platform, overseeing people leaders and individual contributors.
Execute the enterprise data platform strategy, translating direction from the Managing Director and Technical Architect into clear plans, priorities, and outcomes focused on ingestion, structuring, governance, and data trust.
Lead the delivery of lakehouse capabilities including ingestion frameworks (batch and streaming), landing zones, structured/curated layers, storage formats, schema evolution, and compute patterns, in alignment with architectural standards.
Partner with midlevel leaders across Data, Analytics, Finance, Security, and Business teams to onboard sources, define domain data products, and align data contracts (e.g., ODCS), retention policies, and access patterns. Own the quality and reliability guarantee at the serving-layer boundary for downstream analytics consumers, including SLA definitions and data-sharing interfaces (e.g., Delta Sharing).
Establish and operationalize master data management (MDM) capabilities including golden record patterns, match/merge survivorship rules, reference data management, and stewardship workflows.
Define and enforce data quality strategy and controls (profiling, validation rules, anomaly detection, SLAs/SLOs, and remediation processes) to ensure trusted, fit for purpose data.
Drive adoption of platform standards, patterns, and automation for ingestion, transformations, metadata/lineage, schema management, and release/change management, including CI/CD pipelines, infrastructure-as-code, and deployment automation for data platform assets.
Establish and enforce testing standards for data pipelines, including unit testing, integration testing, and end-to-end test automation, to ensure platform reliability and enable confident, frequent releases.
Champion the adoption of AI-assisted development tools and practices (e.g., code generation, automated documentation, test scaffolding) to accelerate engineering velocity and data product delivery.
Implement and govern data security controls at the platform layer, including tokenization, data masking, encryption, and fine-grained access policies to meet regulatory and compliance requirements.
Make high-level decisions that impact platform reliability, scalability, cost, and delivery of prioritized data onboarding and curation initiatives.
Ensure engineering designs and implementations adhere to established standards and that appropriate risk review occurs for security, privacy, resilience, and compliance.
Own hiring, performance management, promotions, and rewards for the Data Platform organization within corporate frameworks.
Manage some contractor resources as required.
Qualifications - Required:
10 years of relevant experience, including leading engineering teams and managing frontline managers.
Strong background building and scaling lakehouse-style data platforms, including ingestion (batch/streaming), data structuring/curation, and governed access patterns; hands-on experience with Databricks Unity Catalog and/or an AWS-native lakehouse (e.g., S3 Iceberg with Glue/Lake Formation/EMR or similar).
Experience with open table formats and lakehouse governance patterns (e.g., Delta Lake and/or Apache Iceberg, cataloging/lineage, fine-grained access controls, and data sharing).
Experience with master data management concepts and implementations (golden record, match/merge, survivorship rules, reference data, stewardship).
Hands-on understanding of data quality management and observability (profiling, validation, monitoring, lineage, and incident response).
Experience implementing CI/CD, infrastructure-as-code, and DevOps practices for data platforms, including deployment automation, environment promotion workflows, and release engineering.
Track record of establishing unit testing, integration testing, and end-to-end test automation for data pipelines and platform components.
Proven ability to lead technical teams through ambiguity and change
Experience delivering technology solutions in complex, regulated environments (financial services a plus)
Bachelor's degree and/or industry?recognized certification, or equivalent experience
Clear, pragmatic communicator with strong execution discipline
Qualifications - Preferred
Experience with enterprise data catalog and lineage tools (e.g., DataHub, Atlan, or similar) integrated across a multi-platform environment.
Familiarity with formal data contract frameworks (e.g., ODCS) for defining and enforcing producer-consumer agreements.
Hands-on experience with MDM platforms (e.g., Profisee or comparable tools).
Experience with data security and tokenization tooling (e.g., Protegrity, DataBolt, or similar) for sensitive data handling in regulated environments.
Demonstrated, habitual use of AI development tools (e.g., GitHub Copilot, Claude Code, Cursor, or similar) to accelerate engineering delivery, with the ability to evangelize these practices across teams.
The Director, Enterprise Data Platform Engineering is responsible for leading the build and ongoing operation of a lakehouse-style data infrastructure that enables reliable, scalable, and governed enterprise data products.
This role leads a technical delivery organization, typically composed of managers and senior level individual contributors, and is accountable for platform capabilities spanning data ingestion, data structuring, master data management (MDM), data quality, data security and servicing layer contracts.
This position balances people leadership and delivery accountability. The Director partners across departments and with enterprise architects on cross-functional initiatives to establish ingestion standards, medallion/zone patterns (e.g., raw/curated/serving), metadata management, and operational controls that improve platform maturity, data trust, and time-to-value for downstream consumers.
Responsibilities
Lead and manage the technical development and delivery function supporting the enterprise lakehouse data platform, overseeing people leaders and individual contributors.
Execute the enterprise data platform strategy, translating direction from the Managing Director and Technical Architect into clear plans, priorities, and outcomes focused on ingestion, structuring, governance, and data trust.
Lead the delivery of lakehouse capabilities including ingestion frameworks (batch and streaming), landing zones, structured/curated layers, storage formats, schema evolution, and compute patterns, in alignment with architectural standards.
Partner with midlevel leaders across Data, Analytics, Finance, Security, and Business teams to onboard sources, define domain data products, and align data contracts (e.g., ODCS), retention policies, and access patterns. Own the quality and reliability guarantee at the serving-layer boundary for downstream analytics consumers, including SLA definitions and data-sharing interfaces (e.g., Delta Sharing).
Establish and operationalize master data management (MDM) capabilities including golden record patterns, match/merge survivorship rules, reference data management, and stewardship workflows.
Define and enforce data quality strategy and controls (profiling, validation rules, anomaly detection, SLAs/SLOs, and remediation processes) to ensure trusted, fit for purpose data.
Drive adoption of platform standards, patterns, and automation for ingestion, transformations, metadata/lineage, schema management, and release/change management, including CI/CD pipelines, infrastructure-as-code, and deployment automation for data platform assets.
Establish and enforce testing standards for data pipelines, including unit testing, integration testing, and end-to-end test automation, to ensure platform reliability and enable confident, frequent releases.
Champion the adoption of AI-assisted development tools and practices (e.g., code generation, automated documentation, test scaffolding) to accelerate engineering velocity and data product delivery.
Implement and govern data security controls at the platform layer, including tokenization, data masking, encryption, and fine-grained access policies to meet regulatory and compliance requirements.
Make high-level decisions that impact platform reliability, scalability, cost, and delivery of prioritized data onboarding and curation initiatives.
Ensure engineering designs and implementations adhere to established standards and that appropriate risk review occurs for security, privacy, resilience, and compliance.
Own hiring, performance management, promotions, and rewards for the Data Platform organization within corporate frameworks.
Manage some contractor resources as required.
Qualifications - Required:
10 years of relevant experience, including leading engineering teams and managing frontline managers.
Strong background building and scaling lakehouse-style data platforms, including ingestion (batch/streaming), data structuring/curation, and governed access patterns; hands-on experience with Databricks Unity Catalog and/or an AWS-native lakehouse (e.g., S3 Iceberg with Glue/Lake Formation/EMR or similar).
Experience with open table formats and lakehouse governance patterns (e.g., Delta Lake and/or Apache Iceberg, cataloging/lineage, fine-grained access controls, and data sharing).
Experience with master data management concepts and implementations (golden record, match/merge, survivorship rules, reference data, stewardship).
Hands-on understanding of data quality management and observability (profiling, validation, monitoring, lineage, and incident response).
Experience implementing CI/CD, infrastructure-as-code, and DevOps practices for data platforms, including deployment automation, environment promotion workflows, and release engineering.
Track record of establishing unit testing, integration testing, and end-to-end test automation for data pipelines and platform components.
Proven ability to lead technical teams through ambiguity and change
Experience delivering technology solutions in complex, regulated environments (financial services a plus)
Bachelor's degree and/or industry?recognized certification, or equivalent experience
Clear, pragmatic communicator with strong execution discipline
Qualifications - Preferred
Experience with enterprise data catalog and lineage tools (e.g., DataHub, Atlan, or similar) integrated across a multi-platform environment.
Familiarity with formal data contract frameworks (e.g., ODCS) for defining and enforcing producer-consumer agreements.
Hands-on experience with MDM platforms (e.g., Profisee or comparable tools).
Experience with data security and tokenization tooling (e.g., Protegrity, DataBolt, or similar) for sensitive data handling in regulated environments.
Demonstrated, habitual use of AI development tools (e.g., GitHub Copilot, Claude Code, Cursor, or similar) to accelerate engineering delivery, with the ability to evangelize these practices across teams.