Data Platform Analyst
Listed on 2026-07-13
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IT/Tech
Data Engineering, Data Analyst
Company Profile
Founded in 1977, GMO is a global investment manager committed to delivering superior long-term investment performance and advice to our clients. We offer investment strategies and solutions where we believe we are positioned to add the greatest value for our investors. These include multi-asset class, equity, fixed income and alternative offerings.
We manage approximately $80bn for a client base that includes many of the world’s most sophisticated institutions, financial intermediaries, and private clients. Industry-wide, we are well known for our focus on valuation-based investing, willingness to take bold positions when conditions warrant, and candid and academically rigorous thought leadership. Jeremy Grantham, GMO’s Co-Founder and Long-Term Investment Strategist, is renowned as an expert in identifying speculative investment bubbles and also as a leading climate investor and advocate.
GMO is privately owned and employs over 430 people worldwide. We are headquartered in Boston, with additional offices in Europe, Asia and Australia. Our company-wide culture emphasizes commitment to clients, intellectual curiosity, and open debate. We celebrate and respect our differences, while embracing and valuing what each of us brings to work, as we know that diverse teams in an inclusive, caring environment achieve higher engagement and better client results.
Position OverviewWe are seeking an experienced Data Platform Analyst to join GMO’s Data Engineering and Operations team within the Technology group. This role offers an opportunity for a seasoned Analyst to leverage their financial reference data and enterprise data platforms experience to evolve GMO’s security master data and portfolio data platforms. Partnering with GMO’s Operations and Investment teams, a successful candidate will translate operational insight into scalable workflow, control, automation, and data quality improvements.
They will bring deep experience in Neoxam’s Data Hub application and a proven track record leading or supporting security master conversions and data platform migrations.
The position will be based in Boston where GMO is currently operating in a hybrid work model, with the current expectation that this person will be in the office for a minimum of 2 days per week and the balance of the week working either in the office or remotely.
Responsibilities- Develop an expert understanding of GMO’s reference data flow within internal databases and vendor applications:
Black Rock Aladdin, Neo Xam Data Hub, and Eagle PACE - Act as the Neo Xam Data Hub application subject matter expert: validate portfolio master and other reference data, resolve production issues, and ensure scalable integration with downstream systems
- Through hands‑on operational execution, identify recurring issues, process gaps, and data quality risks; translate findings into business requirements and recommendations for workflow, control, automation, and data quality improvements
- Analyze data in multiple databases and use a combination of vendor toolsets, Python, SQL, and Excel to develop recommendations to improve data quality
- Support the design, governance, and transformation activities for portfolio, security master, and reference data across enterprise platforms, including data model review, conversion analysis, controls, and cross‑functional stakeholder alignment
- Think critically to help automate or improve processes and procedures (e.g. control reports, daily checklist)
- Create business requirements for the onboarding of new datasets and collaborate with software developers to test and implement data processing/storage
- Explore artificial intelligence tools to enhance existing reference data operations, data quality controls, and production monitoring processes
- Participate in the team’s evolution from manually maintaining data to maintaining and monitoring data definitions, quality service‑level expectations, exception thresholds, and downstream consumption patterns
- Partner with data engineering and business stakeholders to modernize static data quality rules, such as null checks, format checks, and cross‑field validations, by introducing contextual…
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