Lead Data Engineer

UFS Tech

Lead Data Engineer building data pipelines and lakehouses for Navanta, collaborating with AI/ML and security teams to ensure data quality and compliance.

Posted 7/16/2026full-timeRemote • 🇺🇸 United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in data engineering with a focus on designing lakehouse architectures, ensuring data quality, and managing ingestion processes. Proficient in Python and SQL, with a strong background in data modeling and pipeline reliability.

Highest-signal resume keywords

Data EngineeringPython ProgrammingSQL ExpertiseLakehouse ArchitectureData Modeling

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data IngestionData Quality ManagementSchema Drift HandlingData Lineage DocumentationData Contracts DefinitionCDC MechanicsData TransformationPipeline ReliabilityMetric Layer OwnershipRegulatory Data Handling

Tools & Technologies

Apache IcebergDeltaHudiDbtSFTPAI/ML CollaborationQuery Engine

Industry Keywords

Data GovernanceAudit ReadinessAccess ControlsData Quality ThresholdsOperational Source Systems

Tech Stack

Tools & technologies

ApachePythonSQL

About the role

Key responsibilities & impact

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

Requirements

What you’ll need

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.

Benefits

Comp & perks

Health insurance
401(k) matching
Flexible work arrangements
Paid time off
Professional development opportunities