Data Integration Senior Engineer

The Data Integration Senior Engineer serves as Strategic Intelligence's hands-on technical lead for data platform engineering, Data Ops, and enterprise data integration. The role turns governed data capabilities—including master data, entity resolution, semantic models and layers, knowledge graphs, and reusable data products—into secure, production-grade pipelines, APIs, event flows, services, and controlled interfaces that support analytics, automation, AI, and agent-enabled use cases.

The role owns the runtime engineering lifecycle for these capabilities, including integration design, release engineering, deployment automation, environment promotion, observability, incident response, reliability, recoverability, performance, and cost efficiency. The Senior Engineer establishes reusable engineering patterns and replaces brittle point-to-point integrations, manual handoffs, duplicate data stores, and one-off extracts with governed, scalable, and supportable services and delivery mechanisms.

The role operates within established business, data governance, product, analytical, architecture, security, and platform ownership boundaries. Relevant capability and domain owners retain accountability for business meaning, taxonomy and ontology standards, stewardship, data-quality policy, master-data requirements, analytical methods, product priorities, and business outcomes. This role owns the technical integration, release engineering, and production operation of approved data capabilities. Finance-domain analysis, measures, and products are outside this role's remit.

• Serve as the hands-on technical lead for data platform engineering, Data Ops, and enterprise integration, contributing directly to architecture, coding, testing, deployment, troubleshooting, and production support.
• Co-design end-to-end solutions with relevant capability owners across business, data, product, architecture, security, and technology, defining integration boundaries, non-functional requirements, release plans, acceptance criteria, and support models from the outset.
• Develop and operate production-grade batch, near-real-time, and event-driven pipelines, APIs, services, and controlled interfaces connecting approved internal systems, external data sources, enterprise platforms, and downstream consumers.
• Create reusable connectors and integration patterns that support analytics, automation, AI, and agent-enabled use cases while minimizing point-to-point dependencies and duplicated logic.
• Establish and enforce engineering standards for source control, code review, CI/CD, automated testing, documentation, secrets management, environment promotion, release and rollback, recovery, and operational support.
• Define and monitor service levels and non-functional requirements for availability, freshness, latency, throughput, recoverability, security, and cost, with clear runbooks and ownership for production support.
• Automate deployment, monitoring, alerting, logging, and observability for data services and integrations; make metadata, lineage, transformation logic, quality results, assumptions, and limitations transparent and discoverable.
• Diagnose incidents and data-delivery failures, perform root-cause analysis, remediate defects, and improve reliability, performance, recoverability, and cost efficiency across pipelines, APIs, integrations, and platform services.
• Productionize MDM, entity-resolution, semantic-layer, knowledge-graph, and governed data-product capabilities while preserving approved data contracts, versioning, access controls, quality rules, and traceability.
• Manage schema, interface, and data-contract evolution, including impact assessment, backward compatibility, consumer…