Lead Data Engineer

Summary/Objective

The Lead Data Engineer owns the Navanta data backbone — public Call Report data in the early build, and secure ingestion from bank cores into lake houses as each client’s on-premises environment is stood up. Working under the SVP of Technology and Commercial AI and in close partnership with the AI/ML, security, and platform teams, this role builds the architecturally clean, well-modeled, reconcilable data foundation that makes it possible for the Navanta AI platforms to give numbers a banker will act on.

• 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

• 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

• 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

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 role
• 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

• Languages Python, SQL (deep)
• Lakehouse & catalog Apache Iceberg;
  Polaris / Nessie / Lake keeper
• 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)

• Experience with financial or core-banking data, or FFIEC / Call Report data specifically
• Strong SQL Server familiarity
• Data contracts, lineage, and governance practices

• Bachelor’s degree in computer science, mathematics, information systems, or a related field, or equivalent hands-on experience
• Experience in the financial services…