Data Engineer — Data Platform & Integrations

Howard Energy Partners•San Antonio, TX

1d•Onsite

About The Position

This role sits at the center of how we amplify that through data, automation, and the intelligent tools our teams will depend on for years to come. Raw data is everywhere: in our systems, our sensors, our transactions. But data alone doesn't move the business. It has to be shaped into information, refined into evidence, and ultimately owned as knowledge before it becomes something a person can act on. That journey, from source to decision, is what this team is responsible for, and why the work matters. This is a hands-on engineering role on a small team. You will write code, ship pipelines, keep production healthy, and partner with people across Operations, IT, Finance, and the business to turn requirements into durable, reliable data products. The work is meaningful, the ownership is real, and the platform you help build will be a direct part of how Howard grows.

Requirements

Genuine curiosity about the business behind the data. Asks why before how.
Sound judgment and the ability to make good decisions with incomplete information.
Strong communication skills, including the ability to translate technical work for non-technical audiences with patience and clarity.
Comfort working on a small team where ownership is broad and collaboration is constant.
Humility to learn before proposing changes, and confidence to raise real concerns when warranted.
A service mindset. The goal is to make the business better, and the best technical work is the work that earns trust.
Demonstrated ability to navigate ambiguity while maintaining a strong understanding of business operations and data-driven decision-making.
Ability to explain technical work clearly to non-technical audiences, in the room and in writing.
Ability to work on-site in San Antonio, TX.
Bachelor's degree in Computer Science, Information Systems, Data Science, Engineering, or a related technical field required; equivalent professional experience considered in lieu of degree.
3 to 6 years of professional experience in data engineering, software engineering, or a closely related technical discipline.
3 to 6 years of production data engineering experience is the target range for this role, enough to ship real work confidently, with room to grow in responsibility.
3+ years building and maintaining data pipelines or integrations in a real production environment.
Strong Python and SQL, comfortable writing, testing, and debugging production-grade code.
Hands-on experience with the Microsoft Azure data stack: any combination of ADF, Functions, ADLS, Synapse, Databricks, or Container Apps.
Comfortable with Git, CI/CD, and Azure DevOps including branching, pipelines, repos, and releases.

Nice To Haves

Familiarity with medallion/layered architectures (Bronze/Silver/Gold) and data contracts.
Experience with Power BI or other semantic-layer tooling.
Exposure to midstream energy, industrial/OT systems, SCADA, historians, or measurement data.
Familiarity with Airflow, data catalog tooling such as DataHub or Purview, or modern metadata management.
Experience operating a platform end-to-end from initial build through ongoing support.
Experience in energy, industrial, or operations-adjacent environments is a plus.
Relevant professional certifications are welcomed.

Responsibilities

Build and ship production-grade data pipelines and integrations on our Azure platform, connecting the systems that run the business: financial, operational, HR, measurement, and vendor data sources.
Develop and maintain analytics-ready datasets for downstream consumers, gathering requirements from business stakeholders and validating that the logic reflects operational reality.
Build and maintain integrations across enterprise systems, treating the data platform as a central integration point that grows in value as each connection is added.
Participate in proof-of-concepts for new ingestion patterns, modeling approaches, or integrations that support AI and automation use cases, and help productionize the ones that work.
Operate what you build: monitor pipelines, keep them healthy, document runbooks, and follow root causes through to resolution.
Improve the platform as you go with better tests, better documentation, and better observability, so that what gets built stays built.
Occasionally support analytics needs such as Power BI datasets or lightweight Python outputs when it directly accelerates a data product delivery.
Partner with departments throughout the company to understand what they need and translate that into technical solutions that reflect operational reality.
Explain technical concepts clearly to non-technical stakeholders, in the room and in writing.
Gather requirements directly, validate logic against real business conditions, and close the loop with confidence.
Identify opportunities to improve consistency, reliability, and clarity across the data platform as the business grows.
Contribute to the evolution of platform practices: better patterns, better standards, better tooling, without layering in unnecessary complexity.
Surface observations about how we work and bring them forward as conversations, not just problems.
Help ensure the platform scales in a way that stays useful and trusted by the people who depend on it.
Approach your own development the same way you approach the platform: as an ongoing practice of getting sharper, broader, and more capable over time.
Engage actively in mentorship, feedback, and learning opportunities as they arise. Growth here is expected, supported, and built into how we work.