Data Engineer

What you’ll be doing: • Build reliable compliance data foundations • Design, build, and maintain curated datasets and data models (e.g., case, alert, entity, transaction, and risk features) to support compliance controls and analytics. • Own data quality: implement validation checks, monitoring, lineage, and documentation to ensure datasets are accurate, complete, and audit-ready. • Partner with engineering teams to close upstream data gaps and standardize event/instrumentation needed for compliance use cases. Enable and enhance detection systems (TMS/EDD/KYC/Screening) • Translate compliance control requirements into scalable data structures, feature sets, and output tables that support detection logic and downstream triage workflows. • Analyze detection outputs to identify opportunities to improve signal quality and operational efficiency (e.g., reduce non-actionable noise while protecting sensitivity). • Support controlled rollouts of detection enhancements by defining success metrics, measurement approaches, and post-deploy monitoring. Measure control effectiveness and produce auditable reporting • Develop measurement frameworks for compliance control effectiveness (coverage, timeliness, stability, and outcome-based metrics). • Build repeatable reporting pipelines and dashboards that are consumable by Compliance leaders, operations teams, and auditors. • Contribute to governance artifacts (definitions, metric logic, runbooks) to ensure metrics are consistent and defensible. Core qualifications • 2–6+ years of relevant experience in analytics engineering, data engineering, BI engineering, or data-heavy technical roles (level dependent). • Advanced SQL skills (complex transformations, performance tuning, data quality checks). • Experience building and maintaining ETL/ELT pipelines and data models (e.g., dbt, Airflow, or equivalent orchestration/transformation tools). • Strong data modeling fundamentals (modular design, reusable patterns, dimensional modeling concepts). • Working Python skills for automation, pipelines, and analysis • Demonstrated ability to execute on ambiguous, cross-functional projects with clear written and verbal communication. • High ownership mindset with strong attention to detail and comfort operating in regulated environments. Nice to haves • Domain experience in BSA/AML, Transaction Monitoring, sanctions screening, KYC/EDD, fraud, or investigations. • Familiarity with Snowflake/Databricks and modern warehouse/lakehouse patterns. • Experience with GitHub-based workflows, CI/CD for data, and data observability tools. • Experience building dashboards in Looker/Tableau/Superset (or equivalent). • Applied statistics/experimentation, root-cause analysis, or detection performance measurement. • Interest in crypto, blockchain analytics, or on-chain/off-chain data integration.