Role- Technical Architect
Location : Pittsburgh PA
We're hiring someone who can drive investment data from every angle - the analysis, the architecture, the business conversation, and the technical one. Not a pure strategist who hands off, and not a pure engineer who waits for requirements. You'll go from a portfolio manager's messy question to a query, to a data model, to a target-state design, to the discussion that gets it funded and built.
You'll own how investment data - reference, market, portfolio, performance, risk, and alternative data - is analyzed, modeled, architected, governed, and delivered to the people who make investment decisions.
What You'll Do
- Analyze the data yourself - profile, query, and reconcile investment data directly (SQL against Oracle, distributed processing on Hadoop/PySpark) to answer business questions and pressure-test assumptions before they become designs.
- Design the architecture - target-state data models, integration patterns, and data-serving designs (including GraphQL and other API layers) that balance performance, cost, and maintainability.
- Drive the business discussion - turn ambiguous needs from PMs, research, risk, and client reporting into a clear problem statement, prioritized backlog, and outcomes stakeholders care about.
- Drive the technical discussion - lead design reviews with engineering and platform teams; make and defend trade-offs on modeling, storage, processing, and distribution.
- Own the data domains - security master and reference data, market/pricing data, holdings and transactions, benchmarks, performance and attribution, risk, and ESG/alternative data.
- Govern data as a product - ownership, quality SLAs, lineage, and shared definitions so a "position" or an "AUM" number means the same thing everywhere.
- Rationalize vendors and platforms - evaluate market data and platform vendors (Bloomberg, LSEG/Refinitiv, FactSet, MSCI, ICE, Aladdin/other OMS) against coverage, cost, and redundancy.
Required Skills & Experience
- 7 12 years across investment management, financial services data, or related consulting, with real exposure to the buy-side investment lifecycle.
- SQL - advanced query writing and performance tuning.
- Oracle - deep experience against Oracle databases (PL/SQL a plus).
- Hadoop - working knowledge of the big-data ecosystem (HDFS, Hive, etc.).
- PySpark - building and optimizing distributed data processing.
- GraphQL - designing and exposing data through GraphQL / API-based serving layers.
- Working fluency in investment data domains (security master, benchmark files, performance return streams, and how they connect).
- Proven ability as a genuine hybrid - trusted by business stakeholders and respected by engineers.
- Data modeling and architecture experience - able to design a target state, not just critique one.
- Strong written and verbal communication; comfortable moving between a PM, a CDO, and a data engineer in the same afternoon.
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