Big Data Engineer – Financial Crimes & AI Enablement Overview Wells Fargo is expanding its AI and data modernization initiatives and is seeking experienced Big Data Engineers to support critical data sourcing, platform engineering, and modernization efforts within the Financial Crimes organization. These roles are funded to accelerate delivery of new AI‑driven use cases that require strong, reliable data foundations. The work centers on large‑scale data ingestion, conformance, and modernization of legacy systems—including the Newton wires platform—onto the enterprise big data environment. Key Responsibilities Lead end‑to‑end development of batch‑oriented data pipelines, including raw data sourcing, domain‑level conformance, and support for downstream curation teams. Design, build, and optimize large‑scale data processing solutions using PySpark, Scala, Spark, and advanced SQL. Support modernization initiatives such as the Newton wires rebuild, including complex parsing, mapping, and transformation of high‑volume structured and semi‑structured data (CLOB, JSON, SWIFT formats). Apply enterprise‑scale performance engineering patterns (partitioning, sorting, join optimization) to ensure reliability and throughput across petabyte‑scale datasets. Collaborate with platform engineering, financial crimes teams, and AI integration groups to ensure data readiness for advanced analytics and AI use cases. Provide L3 production support for developed pipelines, particularly during go‑live and stabilization phases. Work within established tooling and frameworks, including Autosys, Unix shell scripting, GitHub/CI/CD, JIRA/Agile, FlowMaster (ingestion wrapper), and Dremio (SQL interface). Required Qualifications 5+ years of experience in Big Data engineering within large enterprise environments. Strong hands‑on expertise with PySpark, Scala, Spark, and SQL. Proven experience with data sourcing, ingestion, and conformance on distributed data platforms. Demonstrated ability to own deliverables end‑to‑end, including production support. Experience building and supporting batch pipelines and working with enterprise schedulers (Autosys). Proficiency with Unix shell scripting, GitHub, CI/CD pipelines, and Agile development practices. Preferred Qualifications Experience with financial crimes, risk, or transaction monitoring data domains. Familiarity with modernization of legacy systems (e.g., mainframe to big data migrations). Exposure to AI‑related data preparation or integration (AI experience is a plus, not a requirement). Knowledge of Power BI for reporting support (beneficial for select roles). Prior Wells Fargo experience or returning “boomerang” candidates with strong references.