Data Platform & Engineering Senior Manager

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, DataOps, 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, FinOps, 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, DataOps, 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 DataOps 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 FinOps 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 DevSecOps.
• 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 decisions.
• Build platform services that support business intelligence, reusable data products, advanced analytics, machine learning, AI, and future real-time operational use cases.
• Lead employees, contractors, managed-service providers, and engineering partners; set clear accountability, coach technical leaders, and build succession depth.
• Manage platform and engineering budgets, licenses, cloud consumption, contracts, and vendor performance.
• Establish and report metrics for platform availability, pipeline reliability, delivery throughput, data quality, incident performance, automation, cost, reuse, platform adoption, and technical debt.
• Other duties as assigned.

• Bachelor's degree in Computer Science, Engineering, Management Information Systems, Data Engineering, or a related technical field; equivalent relevant experience will be considered.
• 15+ years of progressive technology experience, including 8+ years leading enterprise data-platform, data-engineering, cloud, or related technical capabilities and 5+ years of people leadership.
• Demonstrated experience leading teams that support modern cloud data architectures such as Microsoft Fabric, Azure lakehouse/warehouse platforms, Databricks, Snowflake, or comparable technologies.
• Deep experience with the Microsoft Azure data ecosystem, including data storage, ingestion, integration, analytics, identity, networking, security, monitoring, DevOps, and platform operations capabilities.
• Strong experience designing and operating data acquisition capabilities across ETL/ELT, change data capture, APIs, event streaming, batch, and near-real-time processing.
• Experience defining enterprise data access patterns, including APIs, governed data sharing, reusable consumption services, and product-serving data layers.
• Experience establishing enterprise data-management practices for metadata, lineage, quality, master/reference data, lifecycle, and governed consumption.
• Experience implementing data-security architectures, including identity, access controls, network isolation, encryption, secrets, auditing, classification, and compliance controls.
• Experience operating production data platforms with defined service levels, monitoring, incident management, support models, backup and recovery, performance management, and cost accountability.
• Experience implementing engineering discipline through CI/CD, source control, automated testing, deployment automation, environment management, and infrastructure as code.
• Experience managing cloud platform economics, consumption optimization, capacity forecasting, cost governance, or FinOps practices.
• Demonstrated ability to partner with Infrastructure, Cybersecurity, Enterprise Architecture, Application, OT, and business leaders across a complex enterprise.
• Experience managing employees, contractors, vendors, budgets, and enterprise technology roadmaps.
• Strong communication, decision-making, problem-solving, and executive-influence skills.
• High level of accountability, customer focus, and ability to balance immediate delivery with long-term platform sustainability.
• Regular and reliable attendance.

• Experience in midstream, oil and gas, chemicals, manufacturing, utilities, or another asset-intensive industrial environment.
• Hands-on experience with Microsoft Fabric, OneLake, ADLS Gen2, Azure Data Factory, Azure Synapse, Event Hubs, Stream Analytics, Azure Functions, Azure DevOps, Entra ID, and Power BI.
• Experience with Databricks, Snowflake, dbt, Kafka, Azure Data Explorer, Kubernetes, and multi-cloud data-platform interoperability.
• Experience ingesting, contextualizing, and managing SAP, Oracle, Maximo, ETRM/commercial, PI historian, SCADA, IoT, and other operational data sources.
• Experience with high-volume time-series data, OT/IT convergence, real-time data, or industrial analytics.
• Experience with enterprise database performance, replication, high availability, disaster recovery, and source-aware ingestion design.
• Experience implementing data catalogs, master-data platforms, data-quality tools, and fine-grained data-security technologies.
• Experience building internal platform products, developer enablement, engineering templates, self-service onboarding, service catalogs, or platform adoption programs.
• Experience supporting AI/ML platforms, MLOps, model-serving, vector or knowledge-store patterns, unstructured data management, or advanced analytics workloads.
• Relevant Microsoft Azure, Databricks, Snowflake, data engineering, architecture, FinOps, or security certifications.

Targa Resources provides equal employment opportunities based on merit, experience, and other work-related criteria and without regard to race, color, ethnicity, religion, national origin, sex, age, pregnancy, disability, veteran status, or any other status protected by applicable law. We also strive to provide reasonable accommodation to employees’ beliefs and practices that do not conflict with Targa’s policies and applicable law. We value the unique contributions that every employee brings to their role with Targa.