Senior Data Software Engineer with Kafka

Responsibilities

• Design and own canonical and semantic data models that create a coherent, product-agnostic view of Oil & Gas data
• Define the contracts and standards that allow data from disparate products to be stitched together reliably and consistently
• Tackle the real-world challenge of integrating data across a broad portfolio of O&G applications, each with its own schema, cadence, and business context
• Identify patterns, resolve conflicts, and build the connective tissue that makes cross-product reporting meaningful
• Architect, deploy, and administer Apache Kafka clusters as the backbone of real-time and event-driven data flows
• Own topic and partition design, security configuration, monitoring, capacity planning, and performance tuning across environments
• Build data foundations ready to support machine learning, LLM-based features, and advanced analytics without requiring a rebuild
• Instrument data flows to understand how data is being produced, consumed, and relied upon by clients
• Design and implement usage tracking, lineage, and observability tooling that gives the business visibility into data health and adoption
• Work closely with product managers, data scientists, and application teams across multiple products

Languages

Required

• Expertise in Apache Kafka, including standing up clusters from scratch and administering them in production, broker configuration, topic and partition design, replication, SASL/SSL security, ACLs, schema registry, monitoring/alerting, and performance tuning at scale
• Proficiency in SQL and Python applied to production data engineering, not just exploration
• Experience designing for downstream use, such as reports, analytics, and AI/ML, with an understanding of how modeling decisions today create constraints or enablement tomorrow

Nice to have

• Strong written communication skills, capable of producing documentation and data contracts that others can operate from without hand-holding
• Familiarity with data observability and telemetry patterns, including lineage tracking, usage instrumentation, and data quality monitoring

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