Data Platform & Engineering Senior Manager

Senior Manager, Data Platform & Engineering

Targa is seeking a hands-on, enterprise-minded Senior Manager, Data Platform & Engineering to lead the technical foundation that supports data, analytics, automation, and AI across the company. This leader will be accountable for modern cloud data platforms, data acquisition, engineering, data management, data security, Data Ops, and enterprise data access services, with an immediate focus on maturing Targa's Microsoft Fabric and Azure-based data environment and establishing reliable production operations.

The role will define the long-term platform and engineering roadmap while delivering near-term stability, scalability, and engineering discipline. The Senior Manager will lead capabilities spanning batch and near-real-time ingestion, ETL/ELT, change data capture, APIs, event-driven integration, medallion architecture, metadata, quality, security, observability, CI/CD, capacity management, platform reliability, operational data contextualization, Fin Ops, and AI-ready data foundations. The leader must be able to operate across Microsoft technologies while maintaining interoperability with platforms such as Databricks and Snowflake.

Job Functions And Key Responsibilities

• Establish and lead Targa's Data Platform & Engineering capability, including organization design, engineering standards, delivery practices, Data Ops, platform operations, and talent development.
• Define and execute the enterprise data-platform roadmap across Microsoft Fabric, Azure data services, lakehouse and warehouse architectures, and interoperable cloud data platforms.
• Establish platform product-management disciplines including service catalog management, platform adoption, customer engagement, roadmap transparency, capacity planning, service onboarding, and business-value realization.
• Own data acquisition capabilities and reference patterns for batch ETL/ELT, change data capture, APIs, file transfer, event streaming, near-real-time ingestion, and operational/industrial data.
• Define enterprise patterns for operational and industrial data acquisition, contextualization, integration, and scalability across historian, telemetry, SCADA, IoT, and future operational data platforms.
• Lead the design and delivery of scalable Bronze, Silver, Gold, and product-serving data layers with clear transformation boundaries, access patterns, quality controls, lifecycle management, and alignment to governed consumption patterns.
• Define and operate enterprise data access services, including APIs, event-driven interfaces, governed data sharing, curated consumption endpoints, and reusable access patterns that enable analytics, applications, AI, and external partner integration.
• Enable self-service data platform capabilities through standardized onboarding, reusable engineering patterns, templates, documentation, developer portals, and governed access mechanisms.
• Establish enterprise data-management capabilities covering metadata, catalog, lineage, data quality, master and reference data, retention, archival, certification, and governed reuse.
• Define enterprise data-lifecycle standards covering acquisition, retention, archival, discovery, disposition, and compliance requirements across structured and unstructured data assets.
• Embed data security into the platform through identity and access management, role-based and attribute-based controls, private connectivity, encryption, secrets management, audit logging, data classification, and policy enforcement.
• Ensure data ingestion and analytical workloads are engineered to protect the performance, availability, and recoverability of operational source systems.
• Lead Data Ops and production platform operations, including monitoring, alerting, observability, capacity management, cost optimization, incident and problem management, runbooks, support coverage, backup, recovery, RTO/RPO, and service-level reporting.
• Establish Data Platform Fin Ops capabilities including consumption monitoring, workload optimization, chargeback or showback models, capacity forecasting, and cost governance across analytical, data engineering, and AI workloads.
• Establish engineering practices for source control, peer review, automated testing, data reconciliation, deployment pipelines, environment promotion, infrastructure as code, release evidence, rollback, and Dev Sec Ops .
• Establish platform capabilities that support enterprise AI adoption, including trusted data foundations, unstructured and semi-structured data management, data discoverability, knowledge assets, and governed access patterns for AI and agentic workloads.
• Partner with Enterprise Architecture, Infrastructure, Cybersecurity, Applications, OT, and source-system teams to define supportable boundaries between analytical workloads and operational application integration.
• Partner with Microsoft and other strategic vendors to validate architecture, capacity, security, regional deployment, interoperability, product-roadmap dependencies, and migration…