Data Lead: Architecture, Ingestion & Platform Enablement

xScion•McLean, VA

4d•Remote

About The Position

xScion is seeking an Data Lead: Architecture, Ingestion & Platform to work on an xScion project team with one of our clients. The candidate leads the end-to-end Data Hub solution architecture and ingestion strategy, translating OEB's goals (resilience, analytics, vendor independence) into a scalable, performant, and testable cloud data platform across AWS, Amazon Redshift, and Databricks.

Requirements

Permanent Residency or US Citizenship
Bachelor's degree in Computer Science, Information Systems, Business IT Management or equivalent practical experience
8+ years in data architecture or data engineering with at least 3 years in cloud data platform roles
Hands-on AWS data services experience: S3, Glue, Glue Data Catalog, Redshift, Athena, or equivalent
Databricks experience required — Unity Catalog, Delta Lake, and ingestion pipeline design strongly preferred
Proficiency designing RBAC and ABAC security models for sensitive or regulated data environments
Strong data modeling skills (logical/physical), schema design, and metadata management
Experience ingesting structured and semi-structured data from third-party or external recordkeeper systems
Ability to produce architecture documentation and standards that can be reviewed and validated by an independent IV&V team.

Nice To Haves

Prior experience with HIPAA-regulated health data or defined benefit/defined contribution plan data
Familiarity with Federal Reserve, OCC, FDIC, or equivalent financial institution security and data standards
Experience with data versioning, lineage tooling, and record storage management at scale
AWS Certified Data Engineer, Solutions Architect, or Databricks certifications.

Responsibilities

Translate RFQ scope and OEB objectives into a solution blueprint spanning ingestion, storage, access, and analytics for DB/DC/Health benefits data
Design the secure, cloud-based Data Hub architecture (AWS, Databricks, Redshift, Glue, cataloging) optimized for performance, scalability, data quality, and resilience
Produce logical and physical data models for structured/semi-structured sources from third-party recordkeeper and internal systems, ensuring IV&V testability
Define standards for file formats, schemas, metadata, data versioning, and reconciliation patterns supporting analytics, IV&V, and audit readiness
Design and prototype ingestion pipelines, transformation jobs, and storage layouts aligned to architecture and IV&V requirements
Advise on query optimization, clustering/partitioning, workload management, cost controls, and monitoring for DB/DC/Health datasets in Redshift and Databricks
Support troubleshooting of data-quality or performance issues identified during IV&V and testing
Partner with Governance, Security, and IV&V leads to ensure delivery within the June–November term and support the November 2026 go-live goal.