POSITION SUMMARY:Targa is seeking a hands-on Data Ingestion Sr. Analyst (Enterprise & OT) to lead how data enters the enterprise data platform. This role owns ingestion across all sources - enterprise applications, third-party data from customers, suppliers, and data providers, and operational technology (OT) sources - and brings deep expertise in getting field and plant data from SCADA, process historians, measurement, and IoT systems across gathering, processing, and transportation assets into the platform reliably, securely, and at the right speed for how the business uses it.
OT data is different from enterprise data: it is high-volume, time-series, often near real-time, and lives behind segmented industrial networks. This Sr. Analyst sets the ingestion architecture and standards for the Data Acquisition pillar, owns the near real-time (low-latency) data architecture, and works closely with automation, measurement, and cybersecurity teams. As AI use cases such as predictive maintenance, anomaly detection, optimization, and operator assistants grow, this role is accountable for making operational data timely, complete, contextualized, and secure enough to power them, and for using AI-assisted tooling to scale the work.
JOB FUNCTIONS AND KEY RESPONSIBILITIES:- Own the enterprise ingestion architecture and pattern library across enterprise applications (e.g., Oracle Fusion Cloud, UKG, OneStream), third-party data, APIs, CDC, file transfer, and OT/operational sources.
- Set ingestion standards, review designs, and prioritize acquisition work across the pillar in line with Ops and Engineering wave and enterprise demand; provide technical direction to the Enterprise Ingestion Engineer and other ingestion contributors.
- Design and build ingestion from process historians, SCADA, measurement systems, and IoT/edge devices into the platform, prioritizing OT data that is gating Ops and Engineering deliverables.
- Choose the right latency tier for each use case (minute-level, hourly, daily) and build the matching patterns, using streaming only where the use case justifies it.
- Own the latency-tier reference architecture, with near real-time (minute-level) as the top tier, including standards for low-latency and event-based data and the path to consumption (Eventhouse/KQL, operational dashboards, alerts, and AI use cases such as anomaly alerts and operational recommendations).
- Define the standard, reusable approach for bringing OT data into the platform from edge to bronze, including patterns for edge processing as some analytics and AI move closer to the asset.
- Land high-volume time-series data with correct timestamps, units, quality flags, and asset context so signals are clean, continuous, and AI-ready for machine learning and analytics.
- Work with Data Foundation to map OT tags and signals to assets, facilities, and equipment hierarchies; use AI-assisted tooling to accelerate tag-to-asset mapping and naming standardization across sites, validating results before adoption.
- Document sources, tag mappings, data flows, and dependencies, and keep runbooks current.
- Build OT data flows that respect industrial network segmentation and IT/OT boundaries; partner with OT cybersecurity and the Data Security Lead on approved connectivity, data diodes or brokers, and access controls.
- Help define governed, secure access to operational data for AI agents and assistants, working with the Platform / Fabric Administrator and Data Security Lead.
- Work with the Platform / Fabric Administrator on retention, tiering, and capacity planning for high-volume time-series and low-latency data.
- Participate in site management of change (MOC) so tag renames, new instrumentation, and system changes are known before they break data flows.
- Monitor OT and near real-time data flows for gaps, latency, and data quality issues; apply automated and AI-based detection of sensor drift, flatlines, and gaps; and work with DataOps on alerting and automated recovery.
- Resolve data issues at the source with automation, measurement, and field teams, and support critical OT and near real-time data flows outside business hours as part of an on-call or escalation rotation.
- Act as the go-to expert for ingestion and OT data across the team; mentor others on OT, low-latency, and time-series data concepts.
- Cross-train a designated backup (Enterprise Ingestion Engineer) on OT sources and low-latency ingestion patterns so OT data does not depend on one person.
- Work with operations, engineering, and measurement stakeholders to understand how OT data is used and what good looks like; guide external partners and vendors on OT data integration work.
- Establish and report metrics for sources onboarded through standard patterns, freshness, completeness, and latency against agreed targets, critical-path delivery for Ops and Engineering milestones, OT data gaps and incidents, and backup readiness.
- Travel periodically to plants, field sites, and control centers to work with operations and automation teams.
- Other duties as assigned.
MINIMUM ESSENTIAL QUALIFICATIONS:- Bachelor's degree in Engineering, Computer Science, Information Systems, or a related field; equivalent relevant experience will be considered.
- 5+ years in data engineering, industrial data integration, or automation, including at least 3 years focused on OT or industrial data.
- Hands-on experience with process historians (for example AVEVA PI / OSIsoft PI, AspenTech IP.21, or similar) and SCADA data.
- Experience with industrial protocols and integration methods such as OPC UA, MQTT, or historian APIs.
- Experience designing and operating low-latency (minute-level) and batch pipelines for high-volume time-series data in production, plus CDC and API ingestion for enterprise applications, on a cloud data platform.
- Hands-on experience with Microsoft Fabric Real-Time Intelligence (Eventstreams, Eventhouse/KQL), Azure Event Hubs, or IoT Hub.
- Hands-on experience with Databricks (Spark, Delta Lake, Auto Loader / Structured Streaming, Unity Catalog) for building and operating ingestion pipelines.
- Strong SQL and Python; experience working with time-series data at scale.
- Working understanding of IT/OT network segmentation and OT cybersecurity principles.
- Experience using AI-assisted development tools (e.g., Copilot) and reviewing and testing generated code.
- Ability to lead technical work and work directly with field, automation, and engineering teams.
- Strong communication, problem-solving, and stakeholder-management skills.
- Regular and reliable attendance, with the ability to travel to field sites and support after-hours escalations.
PREFERRED QUALIFICATIONS:- Experience in midstream, upstream, utilities, chemicals, or other asset-intensive industries.
- Familiarity with gas measurement data and flow computers.
- Experience with asset hierarchies and industrial data context (asset frameworks, ISA-95), including industrial knowledge graphs or asset context models.
- Experience preparing time-series data for machine learning, anomaly detection, or predictive maintenance use cases.
- Exposure to edge computing and industrial DataOps platforms.
- Experience governing secure AI or agent access to operational data.
- Experience with third-party data exchange (SFTP, partner APIs, data shares) and enterprise applications such as Oracle Fusion Cloud, UKG, or OneStream.
- OT security credentials such as ISA/IEC 62443 or GICSP.
- Relevant Microsoft Fabric or Azure data engineering certifications.
EQUAL EMPLOYMENT OPPORTUNITY: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.
