Forward Deployed Data Engineer - US

Reposted 12 Hours Ago
Hiring Remotely in United States
Remote
155K-200K Annually
Mid level
Analytics
The Role
The Forward Deployed Data Engineer will manage AI-automated data platform migration projects, ensuring timely delivery and communication with clients while utilizing proprietary tools.
Summary Generated by Built In
About Datafold

Datafold is the data engineering automation platform. A major part of our business is delivering large-scale data engineering projects such as data platform migrations with AI, at fixed price and guaranteed timeline. We are not a typical services provider or SI – we are a venture-backed software company that reimagined automation from the ground up and has been delivering projects up to 6x faster than alternatives.

We partner heavily with leading data platforms, including Databricks and Snowflake, to deliver large-scale complex migrations.

About the Role

Datafold is hiring a Forward Deployed Data Engineer to own the delivery of our AI-automated data platform migration projects. This is a high-ownership, customer-facing role at the intersection of data engineering, AI, and project leadership.

As a Forward Deployed Data Engineer leveraging Datafold's AI technology, you will own migration projects end-to-end, gain deep understanding of the customer's data platform, manage execution of the project, and remove any blockers on the way for a timely migration delivery.

Using our proprietary AI tooling to plan and deliver the migration projects, you will be doing the work equivalent to a full team of consultants.

What You'll Do
  • Own 1–4 concurrent migration projects end-to-end: scoping, planning, execution, and customer handoff

  • Be the primary customer contact: run weekly check-ins, manage stakeholder expectations, and escalate risks early before they compound

  • Configure Datafold's Migration Agent and oversee the migration execution

  • Partner with Datafold's engineering team to execute migrations

  • Help refine and scale our product and delivery playbook as the team grows

What We're Looking For
  • 3–6 years in data consulting, professional services, or a customer-facing data engineering role

  • Excellent communication skills — equally comfortable in an exec check-in and a technical design session

  • Strong grasp of the modern data stack: dbt, Snowflake, Databricks, orchestration tools, and major patterns (stored procedures, streaming, incremental processing)

  • Extreme ownership mentality — you identify, surface, and fix problems and rally the team to help without being told

  • AI power user — using AI every day and always learning and improving on how to use it more effectively

  • Exposure to legacy data stack and patterns (ETL, stored procedures, etc.) and data platform migration projects is a strong plus

Datafold is an equal opportunity employer and does not discriminate against any employee or applicant for employment based on race, color, religion, sex, national origin, age, disability, genetic information, sexual orientation, gender identity, marital status, military status, or any other protected characteristic. We are committed to providing equal employment opportunities to all individuals. We strive to create an inclusive and diverse work environment where all employees are valued and unique perspectives are respected and celebrated.

Skills Required

  • 3-6 years in data consulting, professional services, or customer-facing data engineering role
  • Excellent communication skills
  • Strong grasp of the modern data stack
  • Extreme ownership mentality
  • AI power user
  • Exposure to legacy data stack and patterns
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The Company
HQ: New York, CA
30 Employees
Year Founded: 2020

What We Do

Datafold is the fastest way to test analytical data during development, deployment & migrations. With Datafold, data engineers can audit their work in minutes without writing tests or custom queries. Integrated into CI, Datafold enables data teams to deploy with full confidence, ship faster, and leave tedious QA and firefighting behind.

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