The Lead Data Engineer owns the Navanta data backbone — public Call Report data in the early build, and secure ingestion from bank cores into lakehouses 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 / 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)
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 industry or a regulated data environment strongly preferred
Full-time role combining ongoing pipeline operations with initiative-based lakehouse build-out and new bank onboarding
Close collaboration with AI/ML, platform engineering, and security teams; on-call rotation covering data pipeline reliability
While performing the duties of this job, the employee is regularly required to sit and use hands to finger, handle, or touch objects, tools, or controls. The employee frequently is required to talk or hear. The employee is occasionally required to stand; walk; and stoop, kneel, crouch, or crawl. The employee must occasionally lift and/or move up to 10 pounds, usually waist high, up to 50 feet away. Specific vision abilities required by this job include close vision and the ability to adjust focus.
Typical office environment
Up to 20% travel time may be required
Navanta is the trusted technology and services partner for community financial institutions, unifying critical systems, security, cloud infrastructure, and support into one seamless, purpose built experience. With more than 35 years of banking expertise — from Managed IT to Core Banking, CRM, and Advisory Services — Navanta helps institutions simplify complexity, reduce risk, and strengthen daily operations. Navanta empowers community bankers and their people to thrive together. Go Bankers, Go.™
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