Lead Data Engineer

Lead Data Engineer position Remote Key Responsibilities Design the lakehouse: Apache Iceberg (or similar technology) on object storage, a catalog for table management and per-bank isolation, dbt models, and a query engine Build secure, least-privilege ingestion from bank systems - log-based CDC where permitted, with query-based and batch/SFTP fallbacks, plus an in-bank collector pattern Own data modeling for the semantic and metric layer (deposits, concentration, uninsured exposure, asset quality, and peer groups) Handle schema drift, data quality, and reconciliation; make ingestion observable and recoverable Partner with the AI/ML team on the structured-query path and with Security on PII classification at landing, in alignment with regulatory data-handling requirements Document data lineage, transformation logic, and access controls to support audit and exam readiness Define and enforce data contracts, quality thresholds, and alerting for pipeline failures Core Competencies End-to-end ownership of ingestion-through-serving pipelines, with a bias toward reliability and observability Rigorous data modeling for analytics - semantic layers, metric definitions, and reconcilable outputs Security and compliance mindset: PII handling, least-privilege access, and data governance aligned to regulatory guidance Cross-functional partnership with AI/ML and platform engineering to deliver governed, queryable data products Key Performance Indicators (KPIs) Data freshness and pipeline reliability - SLAs met for data ingestion and bank-core feeds Data quality score across key metrics versus source reconciliation Time to onboard a new bank's data environment, from kickoff to queryable lakehouse PII classification coverage at landing and zero unauthorized data-access incidents Semantic layer adoption - percentage of assistant queries resolved via governed metrics versus ad hoc SQL Qualifications To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required. 8 12+ years in data engineering with end-to-end ownership of ingestion through serving, and 2+ years in a lead or senior roles Strong Python and expert SQL; rigorous data modeling for analytics Hands-on lakehouse experience (Iceberg/Delta/Hudi or equivalent) and modern transformation tooling Built reliable pipelines from messy operational and transactional source systems Comfort with CDC mechanics and the realities of pulling from databases you do not control Core Technologies Languages: Python, SQL (deep) Lakehouse & catalog: Apache Iceberg; Polaris / Nessie / Lakekeeper Transform & query: dbt; Trino / Presto / DuckDB CDC & streaming: Debezium (SQL Server CDC, Postgres logical replication), Kafka / Redpanda Orchestration: Dagster (or Airflow) Storage: S3 / MinIO SQL Server and PostgreSQL data modeling, pgvector (or equivalent) Nice to Have Experience with financial or core-banking data, or FFIEC / Call Report data specifically Strong SQL Server familiarity Data contracts, lineage, and governance practices Education and/or Experience Bachelor's degree in computer science, mathematics, information systems, or a related field, or equivalent hands-on experience Experience in the financial services industry or a regulated data environment strongly preferred

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