You will join a data-intensive product team responsible for building and operating core data pipelines and backend systems that model and deliver insights on global refinery operations and economics. The team works at the intersection of data engineering, backend engineering, and data science, transforming diverse industrial and market data into reliable, production‑grade datasets used by customers and internal teams. This role is a senior individual contributor position, with significant ownership over data architecture, pipeline reliability, and engineering standards. You will help shape how data is engineered, validated, and delivered across the Refineries product.
Responsibilities
- Design, build, and evolve batch and streaming data pipelines that power refinery modeling, analytics, and customer-facing products.
- Own complex data ingestion, transformation, validation, and delivery workflows across multiple data sources.
- Drive improvements in pipeline reliability, scalability, and observability, including retries, backfills, data quality checks, and monitoring.
- Lead schema design, versioning, and evolution strategies to support stable, long-lived data contracts.
- Build and maintain backend components and APIs used to serve data to downstream systems and applications.
- Partner closely with data scientists, product managers, and other engineers to translate domain requirements into robust technical solutions.
- Continuously improve existing systems as data volume, complexity, and product expectations grow.
Skills And Experience
- Deliver high-quality, well-tested, and maintainable code, setting a strong example for engineering best practices.
- Own significant parts of the data platform end-to-end, from ingestion to production delivery.
- Make architectural contributions to data processing, storage, and delivery patterns.
- Contribute to and improve CI/CD pipelines, automation, and operational tooling.
- Instrument services and pipelines with metrics, logs, and alerts, and help define operational standards.
- Play an active role in incident response, root‑cause analysis, and long-term system improvements.
- Review code, mentor other engineers, and help reinforce shared coding and architectural standards across the team.
Nice to have
- Exposure to Kafka, Spark, or streaming architectures.
- Experience with Kubernetes.
- Familiarity with event-driven or microservices architectures.
- Exposure to analytical datastores (e.g. Elasticsearch).
- Full-stack awareness (e.g. ability to read, review, and provide feedback on frontend or API-layer pull requests, without being a primary frontend contributor).
- Prior experience working on data products in energy, commodities, or industrial domains.
Our People PledgeDon’t meet every single requirement? Research shows that women and people of color are less likely than others to apply if they feel like they don’t match 100% of the job requirements. Don’t let the confidence gap stand in your way, we’d love to hear from you! We understand that experience comes in many different forms and are dedicated to adding new perspectives to the team.Kpler is committed to providing a fair, inclusive and diverse work‑environment. We believe that different perspectives lead to better ideas, and better ideas allow us to better understand the needs and interests of our diverse, global community. We welcome people of different backgrounds, experiences, abilities and perspectives and are an equal opportunity employer.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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