Role- Technical Architect/Investment Data Architect

Location : Pittsburgh PA (Hybrid - 2 OR 3 Days)

Need Someone with Asset Management Background

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.