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Control Systems Data Jobs

4 jobs found

14d ago

Data Engineer

Blackbeard Operating, LLC Fort Worth, TX

Fort Worth, TX

14d ago

Full time Backend Developer Apache Airflow Azure

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Job Description

Blackbeard Operating, LLC is seeking a hands-on Data Engineer to design and maintain the data infrastructure that supports operational and financial decision-making. This role converts production and third-party data into reliable, analytics-ready pipelines and datasets.

Responsibilities

Design, build, and maintain ETL/ELT pipelines that move data from source systems into centralized, analytics-ready datasets, including sources such as SCADA, ComboCurve, SQL Server, and third-party providers.
Integrate external provider data, including IHS/Enverus, ComboCurve, land systems, and AFE or cost tracking platforms, using API-based or file-based ingestion as needed.
Ensure production volumes, well headers, ownership data, and economics are correctly propagated across systems.
Own pipeline failures end-to-end by diagnosing issues, implementing fixes, and preventing recurrence.
Automate manual workflows currently handled in Excel or ad hoc scripts.
Manage and evolve the core SQL Server environment, including schema design, indexing, and performance optimization.
Make practical architectural decisions for data storage, transformation patterns, and tooling without overengineering.
Evaluate and integrate new tools and data sources as company needs evolve.
Implement testing across the pipeline, including ingestion input validation, mid-transform checks (such as expected row counts and value ranges), and regression testing to prevent unintended changes.
Build monitoring and alerting to detect data quality issues before business users are impacted.
Define and enforce data standards, naming conventions, and documentation practices.
Maintain documentation covering pipeline architecture, data lineage, and known data quirks to support ongoing understanding and maintenance.
Partner with source system owners to understand data provenance and resolve issues at the root.
Collaborate with Reservoir Engineering, Finance, Land, and Operations to translate data needs into engineering solutions.
Serve as a technical point of contact for data access, availability, and reliability questions.
Support Analytics Engineering by delivering clean, well-documented data assets.
Challenge how data is recorded and stored across the organization, encouraging structured, queryable formats at the source to reduce downstream cleanup and improve scalability.

Requirements

Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field.
5–10 years of experience in data engineering or a closely related role; minimum 5 years required.
Expert-level SQL with strong hands-on experience using SQL Server.
Proficiency in Python for data transformation, automation, and pipeline development.
Demonstrated experience designing and maintaining production-grade ETL/ELT pipelines.
Experience with orchestration tools such as Airflow or Azure Data Factory; familiarity with dbt and Snowflake is a plus.
Familiarity with upstream oil and gas data (production volumes, well headers, decline curves, AFE/cost data) strongly preferred.
Experience integrating with O&G data providers such as IHS, Enverus, or ComboCurve is a plus.
Comfort working in a lean, fast-moving environment without a large data platform team behind you.
Strong attention to data quality and documentation, with a mindset focused on building pipelines you can trust.
Strong Python proficiency, including hands-on experience with pandas or polars, SQLAlchemy or pyodbc, requests, pydantic, pytest, and Streamlit.
Git proficiency, including branching strategies, pull requests, and code review workflows.
Actively uses Claude and AI coding tools to accelerate development, applying them to real engineering problems.
Effective communication skills to translate technical constraints into plain language for non-technical stakeholders.

Technical Stack

SQL, SQL Server
Python, ETL/ELT
SCADA, ComboCurve, IHS/Enverus
Airflow, Azure Data Factory, dbt, Snowflake
pandas, polars
SQLAlchemy, pyodbc, requests, pydantic, pytest, Streamlit
Git, Claude

What Success Looks Like

Production, operational, and financial data flows reliably from source systems to analytics-ready datasets without manual intervention.
Data quality issues are detected by monitoring before reaching business users.
Reservoir engineers, finance, and operations teams trust the received data and can act with confidence.
Data infrastructure scales alongside the company without accumulating technical debt.
Business users spend less time wrangling data and more time deriving insight.
The engineer is viewed as a trusted partner by technical and operational teams.

Location

Fort Worth, TX (onsite)

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27d ago

Utility Data Analyst

City of Seguin, TX Seguin, TX

Seguin, TX

27d ago

$65k - $65k/yr Full time Analytics Automation Business Analytics

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Job Description

Support utility data engineering and reporting for SSLGC, a wholesale water utility, based in Seguin, TX.

Salary: USD 65,478 (yearly)
Location: Seguin, TX (onsite)
Experience: 1+ years
Pay Group: U13

Responsibilities

Assist the Assistant General Manager with utility application management, data engineering, data modeling, and data visualization for SSLGC
Handle data and application work tied to water treatment, production, transmission, and distribution
Work on business requirement analysis and support procurement of advanced utility software solutions
Support system integrations across GIS, SCADA, and asset management/maintenance systems
Develop new data solutions through a self-starter approach, including asking inquisitive questions about system design, architecture, data types, and regulatory data requirements
Build an in-depth understanding of SSLGC water workflows and the data produced by those processes
Document and identify data flow from each system
Perform routine exploratory data analysis to understand datasets and establish a quality control baseline for production systems
Expose outliers that may drive inaccurate data results
Run data evaluation steps to clean, transform, and analyze data back to production sources
Validate data through routine tests and confirm results with department staff and supervisors
Create weekly reports for Assistant General Manager review, focused on data quality and recommendations to reduce outliers and improve data collection
Attend software development meetings with vendors, project managers, and consultants as needed
Maintain regular interaction with operations and maintenance staff to identify data quality issues
Perform other duties as assigned

Requirements

At least 1 year of experience with GIS and asset management/CMMS, preferably for water utility infrastructure
Working knowledge of data analysis and reporting tools including Microsoft Excel and Power BI
Water utility industry experience is helpful but not required
Bachelor’s or Master’s (preferred) in Geographic Information Systems, Data Analytics, Business Administration, Information Systems, or a related degree
Or any equivalent combination of experience and training that provides the required knowledge, skills, and abilities
Valid Texas Driver’s License with an acceptable driving record

Technologies

Microsoft Excel
Power BI
Geographic Information Systems (GIS)
Supervisory Control and Data Acquisition (SCADA)
Computerized Maintenance Management Systems (CMMS)

Benefits / Open Until Filled

Starting Salary: $65,478.40/year
Position: Will remain open until filled

Physical Ability

Frequently required to stand, walk, kneel, crouch, climb, twist, reach, push, pull, and lift, carry, or move objects or supplies up to 25 lbs

Certificates and Licenses Required

Valid Texas Driver’s License with an acceptable driving record

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1mo ago

Senior Machine Learning Engineer

Xenon7 Columbus, OH

Columbus, OH

1mo ago Closed

Contract Backend Developer Senior APIs

This position is no longer accepting applications (closed Oct 22, 2026).

Job Description

Senior Machine Learning Engineer to design and run production ML systems for a life sciences client in a hybrid environment.

Responsibilities

Design, deploy, and maintain production-grade MLOps pipelines and infrastructure for continuous training, deployment, versioning, and monitoring.
Implement automated model drift detection, performance monitoring, and self-healing inference pipelines in high-reliability environments.
Operationalize production ML models and integrate them into operational technology (OT), API manufacturing workflows, and chemical process control systems.
Deploy predictive models for batch processing, process control optimization, real-time quality assurance, and facility automation use cases.
Build low-latency, high-throughput model serving microservices and architectures using FastAPI, Triton Inference Server, and TorchServe.
Containerize and orchestrate ML workloads across distributed cloud and edge systems with Kubernetes, Docker, and pipeline engines such as Kubeflow and MLflow.
Partner with chemical engineers, computational biologists, and software architects to turn operational friction into production-ready ML solutions.
Define enterprise MLOps standards, model governance, and CI/CD best practices across the full ML lifecycle.

Requirements

10–20+ years of senior-level experience across software engineering, MLOps, production ML deployment, and infrastructure scaling.
Proven ability to deliver production ML systems in highly specialized, non-standard domains, including transitions between process/chemical engineering ML and clinical or scientific research applications.
Must hold unrestricted US work authorization (no sponsorship available).
Ability to work 3 days per week onsite in the Indianapolis, IN area (regional/EST candidates may travel onsite).
Pragmatic problem-solving and strong collaboration; able to explain complex MLOps architecture to cross-functional engineering teams.
Advanced Python and C++ plus deep expertise with PyTorch, TensorFlow, or Scikit-learn.
Demonstrated expertise with Triton Inference Server, TorchServe, MLflow, Kubeflow, or Databricks ML runtime.
Hands-on experience with Kubernetes, Docker, CI/CD pipelines, FastAPI/gRPC, and AWS/Azure ecosystems.
Experience building real-time model monitoring, feature stores, drift detection, and integrations with enterprise data pipelines.
Deep exposure applying ML to either scientific/clinical domains (drug discovery, small or large molecule, computational biology) or chemical/process engineering environments (API manufacturing, batch processing, SCADA/MES integration, process optimization).

Required Technologies

Python, C++, PyTorch, TensorFlow, Scikit-learn
Triton Inference Server, TorchServe, MLflow, Kubeflow, Databricks ML runtime
Kubernetes, Docker, CI/CD pipelines
FastAPI, gRPC, AWS, Azure
Feature stores, SCADA, MES

Location & Contract Details

Location: Columbus, OH (hybrid)
Onsite requirement: 3 days per week onsite in the Indianapolis, IN area
Job type: Contract (Full-Time / enterprise project engagement)
Engagement route: Outsourced via Xenon7

Nice-to-Haves & Certifications

Academic background in Chemical Engineering, Bio-process Engineering, Computer Science, or related STEM discipline.
Experience operationalizing ML models in regulated GxP environments in life sciences or specialty chemicals.
AWS Certified Machine Learning – Specialty, Databricks Certified Machine Learning Professional, or equivalent MLOps credentials.

Role Focus

Not a Data Scientist or exploratory R&D role focused on Jupyter-style modeling or research algorithms.
Not a non-coding architecture position; emphasis is hands-on MLOps and engineering execution, including model deployment and infrastructure creation.
Not fully remote; hybrid onsite commitment is required.

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1mo ago

Asset Management Data Analyst

Cleco Pineville, LA

Pineville, LA

1mo ago Closed

Full time Analytics Asset Management Automation

This position is no longer accepting applications (closed Sep 24, 2026).

Job Description

Cleco is seeking an Asset Management Data Analyst II to connect plant operational signals with commercial context and turn that data into practical analytics for generation asset performance. This role focuses on building and governing analytics solutions that support decision-making through dashboards, time series analysis, workflow optimization, and maintenance-focused insights.

Based onsite in Pineville, LA, the analyst will partner across Asset Management, Generation Operations, Data Analytics/Data Science, and IT to integrate industrial time series from operational systems into business processes and enterprise data initiatives.

What you’ll do

Help drive a corporate culture centered on transparency, integrity, safety, environmental responsibility, employee development, sense of belonging, customer service, and operational excellence.
Accelerate development and deployment of data analytics that support business decisions.
Gather and analyze operational and commercial data to uncover trends, patterns, and actionable insights.
Monitor and report on progress for analytics and data management initiatives.
Apply industry best practices to continuously improve asset performance through analytics.
Collaborate with generation plant teams to understand data requirements, emphasizing time series data from operational systems and integration with business data.
Build and maintain dashboards, reports, and visualizations using BI tools such as Power BI and Tableau.
Use time series analysis to identify trends, anomalies, and opportunities to optimize equipment performance and reliability.
Model asset health metrics and KPIs that inform predictive maintenance and lifecycle strategies.
Support data quality by ensuring time series data is accurate, reliable, and consistent.
Design and maintain data workflows and pipelines, with an emphasis on real-time or near-real-time availability of time series data.
Coordinate with enterprise data teams to align local plant data strategies with broader data governance and architecture initiatives.
Assist in defining an AI/ML roadmap for asset management, including anomaly detection approaches and digital twin simulations.
Architect and leverage an Azure-based data analytics platform to support scalable model development and deployment.
Participate in development and optimization of analytics processes, including integration of operational and business data.

Required qualifications

Bachelor’s degree in Information Systems, Data Analytics, Computer Science, Engineering, or a related field.
3 to 8 years of experience in a data analytics or business intelligence role (3+ years required; 3–8 preferred).
Hands-on experience with BI tools (for example, Power BI, Tableau, Qlik) and proficiency in SQL.
Experience working with time series data from SCADA systems, IoT devices, or other industrial data sources.
Experience in data integration and workflow optimization, particularly when combining operational and business datasets.
Strong analytical and problem-solving skills.
Strong experience in generation / commercial operations.
Proficiency in data visualization tools (for example, Power BI) to create dashboards and reports.
Knowledge of data management practices and tools.
Excellent communication and presentation skills, with the ability to collaborate across cross-functional teams.
Strong organizational and project management skills.
Understanding of asset health metrics, predictive maintenance strategies, and how they improve operational efficiency.
Familiarity with enterprise concepts such as data governance, data quality, and data architecture is a strong plus.
Ability to translate complex data into insights that support asset health management and maintenance strategies.
Experience with Azure data science and big data services (for example, Azure Databricks, Azure Machine Learning, Azure Data Lake) or similar cloud platforms (AWS/GCP).
Progression to this level is restricted based on critical individual capabilities and business requirements, and must be supported by market survey data.

Technologies you may use

Power BI, Tableau, Qlik
SQL
SCADA, IoT
Azure, Azure Databricks, Azure Machine Learning, Azure Data Lake
AWS, GCP

Certifications

Relevant certifications (for example, Certified Business Analysis Professional (CBAP), Power BI Data Analyst Associate Certification) are a plus but not required.

Key competencies

Behavioral: Building Partnerships; Leading Teams; Business Acumen; Communication; Courage; Building Self-Insight; Driving for Results; Energizing the Organization; Driving Execution; Building Trusting Relationships; Driving Innovation; Planning and Organizing; Safety; Establishing Strategic Direction
Technical/Business: Analytical skills; Organizational skills; Strategic Planning; Data Collection and Analysis; Presentation Skills; Business Intelligence skills

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Control Systems Data Jobs

Data Engineer

Utility Data Analyst

Senior Machine Learning Engineer

Asset Management Data Analyst

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