Sr SWE, Fraud Science Data Platform

Delivers complete solutions spanning all phases of the Software Development Lifecycle (SDLC) (design, implementation, testing, delivery and operations), based on definitions from more senior roles. Advises immediate management on project-level issues Guides junior engineers Operates with little day-to-day supervision, making technical decisions based on knowledge of internal conventions and industry best practices Applies knowledge of technical best practices in making decisions Develop and maintain production SQL pipelines in BigQuery that process billions of rows daily, supporting fraud detection, investigations, and ML modeling teams across Global Financial Crimes and Customer Protection. Optimize SQL query performance and reduce runtime execution through query tuning, partitioning/clustering strategies, materialized views, and cost-efficient pipeline design—targeting measurable improvements in slot usage and processing time. Build modular, testable SQL transformations using dbt, Dataform, or similar frameworks that translate investigative heuristics and typology insights into ML-ready features with rapid deployment cycles. Manage and maintain production job scheduling using Airflow/Cloud Composer, including DAG development, dependency management, backfill execution, and monitoring for pipeline reliability. Implement data quality validation frameworks (dbt tests, custom SQL checks, or Great Expectations) to ensure pipeline accuracy, catch data anomalies, and maintain trust in downstream fraud detection systems. Collaborate with data scientists and investigators to understand feature requirements and translate them into efficient, production-ready SQL implementations. Support operational monitoring, troubleshooting, and incident response for production data jobs. Document SQL code and pipeline architecture to enable team knowledge sharing and maintainability. 3+ years relevant experience and a Bachelor's degree OR Any equivalent combination of education and experience. 5+ years of production data engineering experience with focus on building and maintaining SQL-based data pipelines. Strong track record optimizing query performance and reducing execution costs at scale. BigQuery SQL Expertise: Expert-level proficiency in BigQuery SQL including scripting, dynamic query execution, window functions, recursive CTEs, partitioning/clustering, and performance tuning on billion-row datasets. Deep understanding of BigQuery cost optimization including slot management and query profiling. Pipeline Orchestration: Hands-on experience with Airflow/Cloud Composer, dbt, or Dataform for scheduling, dependency management, backfills, and production pipeline operations. Comfortable debugging failed jobs and implementing retry/recovery logic. SQL Performance Optimization: Demonstrated ability to analyze slow-running queries, identify bottlenecks, and implement optimizations that significantly reduce runtime and cost. Experience with query execution plans, materialized views, incremental processing patterns, and partition pruning. Data Modeling Fundamentals: Understanding of dimensional modeling, entity linking, identity resolution, and feature engineering patterns. Ability to design efficient table schemas that balance query performance with storage costs. Software Engineering Practices: Strong fundamentals in Git workflows, code reviews, testing strategies, and documentation. Ability to write clean, modular SQL code that other engineers can understand and maintain. Python for Data Engineering: Proficiency using Python for data processing, pipeline orchestration, and tooling (pandas, numpy). Comfortable writing Python scripts to support SQL pipeline operations. Feature Store Experience: Prior work building or contributing to feature stores, ML data platforms, or analytical data products. Understanding of feature drift, temporal validation, and serving patterns for ML models. Data Quality & Observability: Experience implementing comprehensive data validation frameworks, pipeline monitoring, anomaly detection, or data quality metrics tracking. Advanced BigQuery Features: Experience with BigQuery ML (BQML), stored procedures, or advanced analytical functions. Familiarity with BigQuery billing, reservation pricing, and cost management strategies. Fraud/Financial Crimes Domain: Understanding of fraud typologies, AML red flags, behavioral patterns, or investigative workflows is helpful but not required—we can teach fraud domain knowledge to strong engineers.