Business Technical Architect

Location:
Pittsburgh, PA

We're hiring someone who can drive investment data from every angle—analysis, architecture, business conversation, and 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.

• 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, Fact Set, MSCI, ICE, Aladdin/other OMS) against coverage, cost, and redundancy.

• 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.