Data Engineer at CBTW handling data pipelines and ETL processes using SAS. Collaborating with business stakeholders and ensuring data governance within SAS environments.
About the role
- Senior Data Engineer responsible for data pipelines and backend systems at Kpler. Working with a diverse team to deliver insights on global refinery operations and economics.
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.
Requirements
- 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.
- 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.
Job title
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