Senior Data Engineer, Public Sector

Posted 16 Hours Ago
Be an Early Applicant
Washington, DC, USA
In-Office
150K-259K Annually
Senior level
Artificial Intelligence • Big Data • Machine Learning
The Data Platform for AI: High quality training and validation data for AI applications.
The Role
Design, build, and maintain scalable data warehouses, marts, and BI reporting; create robust data models and pipelines; perform audits and data quality tests; collaborate with cross-functional teams to provide single-source-of-truth analytics and support business decision-making. Requires active security clearance.
Summary Generated by Built In
Senior Data Engineer, Public Sector

As a Data Engineer for the Public Sector business unit, you will build Scale's analytical and business-intelligence infrastructure. Scale's customers process millions of tasks through our APIs, and we're looking for a talented Data Engineer to build scalable solutions to support this growth. You will have widespread purview, with responsibility for understanding, mining, aggregating, and exposing data across the entire business unit to support timely and efficient decision-making and data exploration. You will also implement Scale's data warehouse, data mart, and business intelligence reporting environments, and help users transition their workflows to these systems. 

This role requires collaboration with leadership and cross-functional teams to solve complex problems and develop sustainable, scalable data solutions. Your responsibilities will include both ad-hoc analyses and the creation of core data models and pipelines, directly impacting how Scale operates and evaluates its performance.

You will:
  • Work with operations, finance, and engineering to drive the development of pipelines that provide single-source-of-truth foundational accuracy
  • Continually improve ongoing data pipelines and simplify self-service support for business stakeholders
  • Perform regular system audits, and create data quality tests to ensure complete and accurate reporting of data/metrics
  • Develop repeatable, scalable analytical solutions, such as data models, improved pipelines, or better underlying tables
  • Have an active Secret security clearance (Top Secret preferred)
Ideally You’d Have:
  • 5+ years of relevant work experience in a role requiring application of data modeling and analytic skills
  • Ability to create extensible and scalable data schema and pipelines that lay the foundation for downstream analysis
  • Mastery of SQL and relational databases; experience with programming languages (e.g., Python/R)
  • Experience building a reliable transformation layer and pipelines from ambiguous business processes using tools such DBT to create a foundation for data insights

Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position and may be inclusive of several career levels at Scale; it will be determined during the interview process based on work location and additional factors, including job-related skills, experience, qualifications, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You'll also receive benefits including, but not limited to: comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend.

The base salary range for this full-time position in the location of Washington DC is:
$150,400$259,000 USD

PLEASE NOTE: Our policy requires a 90-day waiting period before reconsidering candidates for the same role. This allows us to ensure a fair and thorough evaluation of all applicants.

About Us:

At Scale, our mission is to develop reliable AI systems for the world's most important decisions. Our products provide the high-quality data and full-stack technologies that power the world's leading models, and help enterprises and governments build, deploy, and oversee AI applications that deliver real impact. We work closely with industry leaders like Meta, Ernst & Young, Mayo Clinic, Time Inc., the Government of Qatar, and U.S. government agencies including the Army and Air Force. We are expanding our team to accelerate the development of AI applications.

We believe that everyone should be able to bring their whole selves to work, which is why we are proud to be an inclusive and equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability status, gender identity or Veteran status. 

We are committed to working with and providing reasonable accommodations to applicants with physical and mental disabilities. If you need assistance and/or a reasonable accommodation in the application or recruiting process due to a disability, please contact us at [email protected]. Please see the United States Department of Labor's Know Your Rights poster for additional information.

We comply with the United States Department of Labor's Pay Transparency provision

PLEASE NOTE: We collect, retain and use personal data for our professional business purposes, including notifying you of job opportunities that may be of interest and sharing with our affiliates. We limit the personal data we collect to that which we believe is appropriate and necessary to manage applicants’ needs, provide our services, and comply with applicable laws. Any information we collect in connection with your application will be treated in accordance with our internal policies and programs designed to protect personal data. Please see our privacy policy for additional information.

Skills Required

  • Active Secret security clearance (Top Secret preferred)
  • 5+ years of relevant work experience applying data modeling and analytic skills
  • Mastery of SQL and relational databases
  • Experience with programming languages (e.g., Python or R)
  • Experience implementing data warehouse, data mart, and business intelligence reporting environments
  • Ability to create extensible and scalable data schema and pipelines
  • Experience building transformation layers and pipelines from ambiguous business processes (e.g., using dbt)
  • Experience creating data quality tests and performing system audits

Scale AI Compensation & Benefits Highlights

The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Scale AI and has not been reviewed or approved by Scale AI.

  • Healthcare Strength Healthcare coverage is described as comprehensive across medical, dental, and vision, with flexibility to choose plans that fit individual or family needs. A monthly wellness stipend further supports physical and mental wellbeing expenses.
  • Equity Value & Accessibility Equity-based compensation is included in eligible packages, positioning ownership as a meaningful component of total rewards for many full-time roles. An employee stock purchase plan also provides an additional pathway to participate in potential upside.
  • Leave & Time Off Breadth Paid time off is positioned as generous with a flexible policy intended to support recharging and burnout prevention. Paid holidays and paid sick days are also part of the time-off offering.

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The Company
HQ: San Francisco, CA
523 Employees
Year Founded: 2016

What We Do

Scale accelerates the development of AI applications by helping machine learning teams generate high-quality ground truth data. Our advanced LiDAR, image, video and NLP annotation APIs allow machine learning teams at companies like OpenAI, Lyft, Pinterest, and Airbnb focus on building differentiated models vs. labeling data.

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