Senior Associate, Data Management - Data & Technology Solutions

StepStone Group•La Jolla, CA

About The Position

StepStone is a global private markets specialist providing investment solutions, advisory services, and data-driven insights to investors. The Data Analytics & Technology Solutions (DATS) department is responsible for building the proprietary technology platform SPI by StepStone and delivering data solutions, automation, and analytics. This role is crucial for maintaining the integrity of DATS's output by managing the collection, ingestion, validation, and mapping of private markets data. The position involves handling unstructured fund manager documents, translating structured data, and leading direct data collection campaigns. It also includes overseeing the consolidation and mapping of data records across various platforms. The individual will work closely with engineering, analytics, and product teams to replace manual efforts with scalable, automated workflows, leveraging technology and AI while maintaining human oversight. This role has direct management responsibility for a team of data management professionals and oversees offshore and vendor capacity.

Requirements

Bachelor's degree in a relevant field
5 to 8+ years in data management, investment operations, or a comparable data-intensive function, preferably within private markets or alternative asset management
Demonstrated experience designing and implementing data workflows at scale, with a track record of specific processes automated and the gains that resulted
Direct people management experience, including managing offshore or vendor delivery
Hands-on experience applying technology (and ideally AI) to operational data problems, including document extraction, data classification, mapping, and quality controls to ensure reliability
Working knowledge of private markets data, including fund structures, cash flows, performance reporting, and portfolio company financials
Process design skills, including the ability to look at a manual workflow, see the version that should exist, and build the plan to get there
Genuine technical fluency with the ability to read and write SQL, reason about data models and APIs, work with modern data platforms (e.g., Snowflake, Power BI, Power Platform), and hold a credible conversation with engineers
Practical command of the current AI tooling landscape and honest judgment about where it is reliable enough to put in front of client-facing data
Exceptional attention to detail, paired with the discipline to build controls that catch errors systematically rather than relying on individual vigilance
Strong analytical and critical thinking skills; able to structure ambiguous problems and cut through to root causes
Clear written and interpersonal communication, with the ability to represent the function credibly with senior stakeholders
Ability to work independently and perform under deadlines in a dynamic environment
Candidates must be at least 18 years old to apply.

Responsibilities

Owns the end-to-end design, execution, and quality of data management workflows within the team’s scope, from intake and extraction through validation, mapping, and release to downstream systems and clients.
Establishes the control framework for data quality: accuracy and completeness standards, automated checks and exception queues, and accountability to defined SLAs.
Leads the application of AI and automation across its data management processes, including data extraction, classification, entity resolution, and mapping, while evaluating technologies, designing human-in-the-loop workflows, and measuring accuracy gains and efficiency improvements against manual processes.
Assesses current-state workflows to find the root causes of rework and error, and delivers redesigned processes with documented procedures, controls, and measurable throughput gains.
Governs the data mapping framework that connects source data to StepStone’s enterprise data model, including taxonomies, entity hierarchies, identifiers, and the processes for proposing, approving, versioning, and maintaining mapping rules.
Manages and develops a team across onshore, offshore, and vendor resources. Sets priorities and capacity plans and builds training and quality review programs.
Partners with DATS engineering, analytics, and product teams to translate recurring operational pain into platform capability, writing clear requirements and following through to adoption.
Serves as the escalation point for data quality issues raised across investment, finance, reporting, and client-facing teams, driving resolution to root cause rather than symptom.
Defines, tracks, and reports operational KPIs across volume, cycle time, quality, cost, and automation, using performance trends to determine where the team should focus its next investments and improvement efforts.