Shield AI is a venture-backed defense-tech company focused on protecting service members and civilians with intelligent systems. The Senior Staff Data Engineer will build and operate the enterprise lakehouse on Databricks, ensuring scalable data ingestion and processing while maintaining strong technical controls and data governance.

Responsibilities:

Design and build ingestion pipelines, batch and streaming where appropriate, from enterprise source systems into the Databricks lakehouse using Delta Lake
Own Bronze-layer ingestion, including raw landing patterns, metadata capture, load traceability, and recoverable ingestion design
Build Silver-layer pipelines for cleansing, standardization, deduplication, conformance, and quality enforcement without embedding unauthorized or undocumented KPI logic
Define and evolve reusable ingestion and transformation patterns, templates, and engineering standards that other domains can adopt as they onboard to the platform
Implement and maintain Databricks platform constructs needed for secure delivery, including catalogs, schemas, service principals, job orchestration, and environment-aware deployment patterns
Build and maintain CI/CD pipelines for data platform assets, including ingestion code, transformation logic, workflow definitions, tests, and environment promotion across dev, test, and prod
Apply data classification, segregation, and handling requirements within the pipeline design, ensuring sensitive and regulated data is processed in accordance with enterprise controls and access policies
Build data quality controls that do more than detect technical failures, including checks that reflect actual business meaning, record integrity, completeness, and expected domain behavior
Quarantine, flag, and route problematic records or datasets according to defined quality and compliance rules rather than silently dropping or obscuring issues
Maintain documentation for source objects, ingestion logic, applied transformations, data quality rules, and known limitations so downstream teams can trust and use the data correctly
Partner with the Analytics Engineer and domain teams to ensure Silver-layer data is reliable, well-governed, and suitable for trusted Gold-layer modeling
Collaborate with domain engineering teams to align on ownership boundaries, onboarding patterns, data contracts, and support expectations as new domains are enabled onto the platform

Requirements:

12+ years of data engineering experience, including hands-on ownership of production data pipelines
Strong Databricks experience, including Delta Lake, Databricks Workflows or Jobs, and Spark with PySpark and/or Spark SQL
Working knowledge of Unity Catalog, including catalogs, schemas, tables, lineage, and access control concepts
Experience with batch, CDC, and/or streaming ingestion patterns and the operational trade-offs associated with each
Experience with CI/CD and deployment automation for data pipelines and platform assets, including version control, testing, and controlled promotion across environments
Strong SQL skills and solid grounding in data modeling fundamentals, even if dimensional modeling is not the primary responsibility of this role
Demonstrated ability to understand the business and regulatory context of the data being processed, not just the mechanics of pipeline development
Experience applying data classification, segregation, security, retention, or compliance requirements in data engineering workflows within a regulated or security-sensitive environment
Ability to design pipelines with awareness of the actual data domains involved, including sensitivity, ownership, permitted use, and downstream impact
Comfort operating in a fast-moving platform build where patterns are still being established and engineers are expected to shape standards, not just follow them
Databricks certification such as Data Engineer Associate or Professional
Experience standing up or maturing Unity Catalog structures and access models in an enterprise setting
Experience with CI/CD, infrastructure-as-code, and deployment automation for data platforms
Experience in defense, aerospace, financial services, healthcare, or another highly regulated environment
Experience enabling multiple business domains on a shared data platform while maintaining strong governance and ownership boundaries

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