Business Technical Architect

Role- Business 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.