Senior Staff Data Engineer, Databricks

United States

Actively hiring, first seen by us today

Pay: USD 145,000–220,000/year — pay source
Salary 145,000 – 220,000 USD per year #LI-KE1
Read the full posting
Work setting: On-site — work setting source
Work setup in the employer’s posting: onsite
From the employer’s posting
Employment: Full-time — employment source
Employment type Full Time Employee
Employment type in the employer’s posting: Full Time Employee
From the employer’s posting

What you’ll work on

Full posting

Design batch and streaming ingestion pipelines into Databricks using Delta Lake
Own Bronze-layer ingestion with raw landing, metadata capture, and recoverable design
Build Silver-layer cleansing, deduplication, and quality enforcement pipelines
Build CI/CD pipelines for data platform assets across dev, test, and prod
Quarantine and route problematic records per defined quality and compliance rules

From the employer’s posting

The Senior Staff Data Engineer will help build and operate the enterprise lakehouse on Databricks, creating the governed data foundation that supports multiple business domains and downstream analytics. This is a hands-on role, responsible for scalable ingestion, reliable data processing, and strong technical controls across the Bronze and Silver layers of the medallion architecture.

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

Build and maintain CI/CD pipelines for data platform assets

Quarantine, flag, and route problematic records or datasets according to defined quality and compliance rules

What you’ll bring

All qualifications

Core experience

12+ years of data engineering experience, including hands-on ownership of production data pipelines.
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.

Preferred experience

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.

Qualification wording

12+ years of data engineering experience, including hands-on ownership of production data pipelines.

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.

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.

Apply at Shieldai

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Actively hiringFirst seen by us today, still listed, and not a repost of an earlier posting.

Status in our records: Live
First seen by us: Nov 3, 2026today
Recorded sightings: 2
Last seen by us: Nov 4, 2026

These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.

How this job compares

Pay

In line with similar jobs

$145K–$220K against $127K–$190K, the middle half of what similar US postings state.

From 2,133 postings in the last year. Stated ranges, not salaries paid.

Time since we first saw it

Day 0

Early. Similar jobs usually come down between day 5 and day 54.

From 10,704 similar postings that came down. Coming down is not always a hire.

Shieldai is hiring

11 open jobs

Open jobs by the week we first saw them, last 12 weeks.

Data Engineer hiring is picking up
Its jobs tend to stay up a long time

Hiring record

See how this role fits your experience

Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.

SQL
Databricks
Delta Lake
Spark
PySpark

Where the posting names them

- 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 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.

Role Summary The Senior Staff Data Engineer will help build and operate the enterprise lakehouse on Databricks, creating the governed data foundation that supports multiple business domains and downstream analytics. This is a hands-on role, responsible for scalable ingestion, reliable data processing, and strong technical controls across the Bronze and Silver layers of the medallion architecture. This role is not limited to moving data from point A to point B. The Data Engineer is expected to understand the meaning, sensitivity, classification, and intended use of the data flowing through the pipelines they build, and to apply that understanding when designing controls, access patterns, data quality checks, and segregation boundaries appropriate for a highly regulated environment.

What You'll Do: - 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.

- 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.

Preferred Qualifications: - Databricks certification such as Data Engineer Associate or Professional. - Experience standing up or maturing Unity Catalog structures and access models in an enterprise setting.

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About the role

We file this job under Data Engineer, by the work the posting describes.

Data engineers build and run the pipelines that copy data out of apps and outside services into a warehouse or lake, and fix them when a source changes or a load fails.

Expect SQL, Python, a cloud platform, a scheduler such as Airflow, and often tools for large data such as Spark or Kafka.

An analytics engineer works one step later, turning the raw data a data engineer lands into clean tables people can query.

US government figures for database architects

Median yearly pay, May 2025: $139,500
Expected change, 2025 to 2035: 9.4% more jobsagainst 3.5% for all jobs

The government has no code for data engineers, so these figures are for the closest group, database architects, which includes data integration and data warehousing specialists.

Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1243.

Job description

View original posting ↗

Shield AI is a venture-backed defense-tech company with the mission of protecting service members and civilians with intelligent systems. Its products include Hivemind autonomy software, V-BAT and X-BAT aircraft, and Aechelon simulation and synthetic reality technologies. With offices and facilities across the U.S., Europe, the Middle East, and Asia-Pacific, Shield AI’s technology actively supports operations worldwide. For more information, visit www.shield.ai. Follow Shield AI on LinkedIn, X, Instagram, and YouTube.

Role Summary

The Senior Staff Data Engineer will help build and operate the enterprise lakehouse on Databricks, creating the governed data foundation that supports multiple business domains and downstream analytics. This is a hands-on role, responsible for scalable ingestion, reliable data processing, and strong technical controls across the Bronze and Silver layers of the medallion architecture.

This role is not limited to moving data from point A to point B. The Data Engineer is expected to understand the meaning, sensitivity, classification, and intended use of the data flowing through the pipelines they build, and to apply that understanding when designing controls, access patterns, data quality checks, and segregation boundaries appropriate for a highly regulated environment.

What You'll Do:

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.

Preferred Qualifications:

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.

Required Qualifications:

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.

Salary

145,000 – 220,000 USD per year

#LI-KE1

#LE

Full-time regular employee offer package:

Pay within range listed + Bonus + Benefits + Equity

Temporary employee offer package:

Pay within range listed above + temporary benefits package (applicable after 60 days of employment)

Salary compensation is influenced by a wide array of factors including but not limited to skill set, level of experience, licenses and certifications, and specific work location. All offers are contingent on a cleared background and possible reference check. Military fellows and part-time employees are not eligible for benefits. Please speak to your talent acquisition representative for more information.

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Shield AI is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, marital status, disability, gender identity or Veteran status. If you have a disability or special need that requires accommodation, please let us know.

Employment type

Full Time Employee

Source details

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Pay

Salary 145,000 – 220,000 USD per year #LI-KE1

Location & working pattern