Job Locations US-MA-Boston | US-Remote

ID

2026-261981

Position Type

Full-Time

Job Grade

20

Department

0001-03433 LMI Technology

Market

Corporate Center

Minimum Salary

USD $156,000.00/Yr.

Maximum Salary

USD $281,000.00/Yr.

Recruiter

Monica Vesprani

Internal Application Deadline

10/8/2026 - 5:00pm ET

Referral Bonus Eligible?

No

The Senior Solutions Engineer, Data is a senior technical leadership role in the Data, Intelligence & Automation domain with LMI-wide scope for data engineering. It defines data engineering practices, standards, and architecture patterns used across LMI Technology, leads the adoption of AI and agentic capabilities across the full SDLC, and deploys AI directly to build robust well-architected solutions that solve business needs — thinking and acting across LMI as a whole rather than any single team or domain. With experience and expertise in data warehousing and data management, and a strong investment domain knowledge combined with deep hands-on technical skills this role will collaborate with and contribute to the whole LM enterprise.

This role’s business solutions delivery is centered on the domain, which serves all of LMI by evolving the One Portfolio Warehouse and providing shared data solutions other domains consume. In close partnership with the LMI Data Office, one of the domain's key business and product partners, the role delivers Shared Data solutions including the One Portfolio warehouse with governance engineered in at every level. It provides technical oversight and integrated data, system, technical, and product recommendations for the most complex and strategic initiatives, drives solution design in the domain and in other domains building on shared data, shapes LMI's multi-year data engineering strategy, and is recognized across LMI Technology as a critical role driving on how AI-native, production-grade data systems are built.

• Leads LMI's AI and agent engineering agenda for data. Architects AI-native data and automation solutions, embeds agents throughout the SDLC — requirements, design, code generation, testing, deployment, operations — and deploys AI to solve business problems directly. Sets LMI's standard for AI engineering in data and drives adoption by every engineer through market place offerings, patterns, tooling, guardrails, and evaluation, measuring impact.
• Establishes and drives data engineering practices across LMI. Defines and drives the data engineering standards used by every domain — coding, data modeling, naming, pipeline patterns, API and data contracts, documentation — and, with Architects, ensures consistent adoption rather than leaving them to each team.
• Champions CI/CD, testing, and automation as the default. Drives fully automated build, test, and deployment pipelines for data products across LMI; makes automated unit, integration, regression, and data quality testing a condition of delivery; promotes infrastructure-as-code, observability, and automated operations so change is safe and fast.
• Engineers data governance into the platform. Partners with the LMI Data Office to turn governance policies, data quality guidelines, stewardship, and critical data element (CDE) tracking into automated controls — lineage, quality checks, access, observability — built into pipelines and data products as patterns every domain adopts.
• Builds and evolves the One Portfolio Warehouse and shared data solutions. Matures the One Portfolio data products — Holdings, Transactions & Cash, Profit & Loss, Companies & Legal Entities, and those that follow; defines data sourcing, solutioning, and technical acceptance criteria so epics are actionable and testable; orchestrates scalable, reusable data products that minimize duplication across LMI while balancing legacy support with new capabilities.
• Thinks and works broadly. Collaborates with and contributes to the broader LM enterprise data community, sharing and adopting common, reusable practices and solutions across domains to accelerate value delivery.
• Consults with technology leaders, business partners, and peers across LMI on long- and short-range data product features; oversees the domain's engineering backlog and guides other domains on consuming shared data; forecasts business and technology trends to set LMI's data engineering direction as AI evolves.
• Drives adoption of the well-architected framework (operational excellence, security, reliability, performance efficiency, cost optimization) as cultural norms; recommends process improvements where AI and automation remove friction; participates in incident management, recommending resolutions and driving root-cause remediation and preventive automation.
• Maintains strategic partnerships with the LMI Data Office, investment stakeholders, product squads, quants, vendors, and technology teams; mentors data engineers across LMI in AI fluency and modern data engineering; shares expertise so teams improve together, demonstrated through publications of knowledge artifacts, speaking in technical forums, and other contributions.

Experience

• 12–15+ years of hands-on data design, engineering, and solution development, at least 5 in technical leadership or solutions engineering; prior financial services or investment management experience is a must.
• Bachelor's or Master's degree in a technical, data, or business discipline, or equivalent experience.
• Advanced knowledge of agile methodologies and proven experience with agile practices — iterative development, incremental value delivery, and continuous improvement — across cross-functional teams.
• Demonstrated experience defining architecture roadmaps, business cases, and implementations for enterprise data solutions in cloud/hybrid architectures; track record of practices defined and adopted across teams and domains, and of owning outcomes across data products and business areas.

Technical

• Extensive experience in data engineering languages and tools, with expert-level foundations in data warehousing and data management — modeling, pipeline and orchestration design, data quality, metadata, lifecycle management — and modern cloud data architecture. In-depth knowledge of emerging technologies, architectural principles, layered/shared solution design, and architecture components; security minded.
• Proven hands-on experience using, designing, developing and deploying AI and agentic solutions is strongly preferred — LLM integration, agentic workflows, retrieval and context engineering over enterprise data, and AI coding, testing, and operations agents in the SDLC.
• Deep, hands-on experience with CI/CD, automated testing, infrastructure-as-code, DevOps/DataOps, observability, and version control, and with implementing data governance controls (quality, lineage, catalog, CDE, stewardship).
• Required tooling: Expertise in Snowflake, SQL, AWS and Python; hands-on GitHub Copilot or similar AI coding assistants; experience working with data quality (e.g., iCEDQ) and data cataloging (e.g., Collibra) tools.
• Enterprise scheduling tools such as Stonebranch or ActiveBatch; building and consuming Data APIs; strong understanding of data lake architectures; familiarity with data streaming.

Domain, Stakeholder & Leadership

• Working knowledge of investment data — holdings, transactions and cash, P&L, entity/company reference data, market and index data — and how liquid markets, risk, and capital allocation functions use it; engages credibly with the Data Office and business on their problems, not just requirements. In-depth knowledge of business operations, objectives, strategies, and global financial services and technology trends.
• Strong partnership, influencing, negotiation, and consulting skills with a practical, pragmatic, outcome-driven mindset; drives change and adapts quickly; recognized thought leader within the company; balances the long-term “big picture” with short-term implications. Strong written and verbal communication with product, business, and senior leadership; collaborates effectively across all levels and diverse backgrounds.

NOTE: This role is open to US based employees only

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