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Lead Data Engineer

Navanta, LLC Alpharetta, GA

Alpharetta, GA

Today

Full time Apache Airflow Data Architecture Data Build Tool

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Job Description

Navanta, LLC builds governed data foundations that support public Call Report data and secure bank-core ingestion as client environments are stood up. As the Lead Data Engineer, you’ll own the lakehouse backbone, working closely with AI/ML, Security, and platform teams to enable reliable, reconcilable data products for Navanta AI platforms.

What you’ll work on

Design and operate the lakehouse using Apache Iceberg (or similar) on object storage, including a catalog for table management and per-bank isolation, plus dbt models and a query engine.
Build secure, least-privilege ingestion from bank systems, using log-based CDC where permitted, with query-based and batch/SFTP fallbacks, and an in-bank collector pattern.
Own data modeling for the semantic and metric layer, covering deposits, concentration, uninsured exposure, asset quality, and peer groups.
Ensure correctness and resilience by handling schema drift, data quality, and reconciliation, while making ingestion observable and recoverable.
Partner across teams on the structured-query path with AI/ML and on PII classification at landing with Security, aligned with regulatory data-handling requirements.
Document for audit readiness by maintaining data lineage, transformation logic, and access controls to support exam and audit processes.
Define and enforce data contracts, including quality thresholds and alerting for pipeline failures.

Core expectations

This is a full-time role that combines ongoing pipeline operations with initiative-based lakehouse build-out and new bank onboarding. You will collaborate closely with AI/ML, platform engineering, and Security, including participation in an on-call rotation for data pipeline reliability.

What you bring

8–12+ years in data engineering with end-to-end ownership from ingestion through serving, including 2+ years in a lead or senior role.
Strong Python and expert SQL, with rigorous data modeling for analytics.
Hands-on lakehouse experience (Iceberg/Delta/Hudi or equivalent) and modern transformation tooling.
Experience building reliable pipelines from messy operational and transactional source systems.
Comfort with CDC mechanics and extracting data from databases you do not control.
Bachelor’s degree in computer science, mathematics, information systems, or a related field, or equivalent hands-on experience.
Financial services or regulated data environment experience strongly preferred.

Key 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 a 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.

Technologies you’ll use

Python, SQL, Apache Iceberg (plus Polaris / Nessie / Lakekeeper), dbt, Trino / Presto / DuckDB, Debezium, Kafka / Redpanda, Dagster (or Airflow), and storage such as S3 / MinIO. Source and modeling include SQL Server and PostgreSQL, plus pgvector (or equivalent).

Nice to have

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

Location and work environment

Alpharetta, GA (onsite). Typical office environment with up to 20% travel time may be required.

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