Data Integration Senior Engineer - Open to Various Locations

Reed Smith LLP is hiring a Data Integration Senior Engineer to lead hands-on data platform engineering, DataOps, and enterprise integration. The role builds and operates governed, production-grade pipelines, APIs, services, and interfaces, and owns their integration design, release engineering, reliability, and production support.

Responsibilities:

• Serve as the hands-on technical lead for data platform engineering, DataOps, 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 communication, and coordinated deprecation of legacy interfaces
• Refactor or retire brittle integrations, manual handoffs, duplicate data stores, and one-off extracts in favor of reusable, governed, and supportable services that reduce operational risk and technical debt
• Partner with the functions responsible for enterprise architecture, platform engineering, information security, privacy, records management, and application ownership to align solutions with architecture, access-control, platform, and compliance standards and to surface capability gaps
• Work with the capabilities responsible for master data, entity resolution, semantic modeling, knowledge graphs, and governed data products throughout design, testing, release, and support. This role owns deployment and runtime reliability; the appropriate functional owners remain accountable for the correctness and evolution of underlying rules, models, definitions, and data-product logic
• Partner with product and portfolio stakeholders on feasibility, dependencies, effort, risk, sequencing, and build-buy-partner decisions; portfolio prioritization, product lifecycle, adoption, and value realization remain with the appropriate product and business owners
• Partner with analytics, reporting, automation, AI, and other approved consumers to deliver governed data through production interfaces, while the appropriate domain owners retain analytical methods, measures, application configuration, interpretation, and outcomes
• Manage technical vendors, contractors, and implementation partners; contribute to platform capacity planning, cost management, performance optimization, and tool evaluation; and ensure engineering quality, documentation, supportability, and knowledge transfer
• Perform other duties as assigned

Requirements:

• Bachelor's degree in computer science, data engineering, information systems, or a related field
• Minimum 7 years of experience in data engineering, integration engineering, data platform engineering, or enterprise data management, including hands-on technical leadership in a complex organization
• Demonstrated experience architecting, building, deploying, and operating production data pipelines, APIs, and integrations on a modern cloud data platform such as Snowflake or a comparable platform
• Experience establishing engineering standards and production practices for automated testing, release management, secrets management, observability, incident response, root-cause analysis, recovery, and operational support
• Experience managing schema and interface evolution, data contracts, metadata, lineage, and backward compatibility across producers and consumers
• Experience working within enterprise architecture, security, privacy, records-management, access-control, and platform requirements and partnering effectively with application and infrastructure teams
• Hands-on engineering leadership with the ability to move from architecture and design to code, testing, deployment, production troubleshooting, and operational support
• Strong hands-on SQL skills and experience with a programming language such as Python, together with orchestration, transformation, API and event-integration, version-control, and CI/CD tooling
• Strong systems thinking across source applications, data models, integration patterns, platforms, security controls, and downstream consumption, with the ability to identify root causes and make pragmatic trade-offs across quality, speed, cost, risk, and maintainability
• Ability to design modular, reusable, secure, observable, and scalable pipelines, APIs, integration services, and platform patterns rather than one-off extracts or manual handoffs
• Strong understanding of DataOps, DevOps, CI/CD, production operations, service reliability, and platform engineering, with sufficient familiarity with MDM, semantic, and data-product concepts to deploy and operate those capabilities effectively
• Clear written and verbal communication skills, including the ability to explain technical choices, constraints, support implications, and risks to business, data, analytics, product, and technology stakeholders
• High degree of professionalism, discretion, sound judgment, flexibility, and adaptability in complex, dynamic, or rapidly evolving situations. Ï
• Master's degree preferred
• Experience deploying and operating MDM, semantic, knowledge-graph, or reusable data-product services in production environments strongly preferred
• Experience in professional services, legal, or another complex, matrixed environment preferred