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Datafold

Automated data quality testing platform

Data Scientist - Migration Intelligence

Data Science +1Confirmed live in the last 24 hours
Full-timeRemote in USA$155k /yr

About the Job

Requirements

  • Master's Degree in Data Science, Applied Mathematics, or a closely related quantitative field.
  • At least 2 years of previous experience in Data Science, Technology Evaluation, or Applied Analytical roles, including performance assessment of AI/ML technologies.

Responsibilities

  • Evaluate third-party tools, platforms, and emerging technologies relevant to data migration, data infrastructure, and AI-assisted workflows, including technical benchmarking, integration feasibility analysis, and build-versus-buy assessment.
  • Develop and apply quantitative models and analytical frameworks to assess migration risk, predict data quality issues, and validate data consistency across source and target database environments throughout migration project delivery.
  • Coordinate end-to-end delivery of customer data migration projects, including scoping, sequencing, milestone planning, risk identification, dependency management, and cross-functional execution across internal teams, customers, and external partners.
  • Design, build, and maintain internal data pipelines and supporting infrastructure that integrate diverse structured and unstructured data sources for analytics, reporting, and operational use cases.
  • Develop repeatable analytical playbooks, migration frameworks, and data-driven operational accelerators based on hands-on project delivery experience.
  • Implement data quality monitoring, automated regression validation, and lineage analysis workflows using Python and SQL to support platform development and customer migration engagements.
  • Evaluate cloud and multi-cloud data environments for cost efficiency, reliability, and integration risk, producing analytical recommendations that inform architectural and sourcing decisions.
  • Implement data models, dashboards, and internal reporting structures that unify multiple information sources into actionable insights for product, sales, and operational teams.
  • Support technology partnerships and external cooperation efforts by assessing integration opportunities and coordinating the technically sound implementation of partner solutions.
  • Act as an internal subject matter resource for data pipeline evaluation and migration methodologies, validating analytical workflows on internal data and translating operational findings into structured product feedback.

About the Company

Datafold

Automated data quality testing platform

Summary of Datafold 1) What it does: Datafold is a platform for data quality that combines automated testing and observability to prevent data problems. It helps data teams make sure data is correct and reliable by catching issues before they affect the data warehouse. 2) How the product works: Datafold plugs into the data development cycle and runs automated tests during key moments—deployments, migrations, and ongoing monitoring. This setup checks data pipelines and code changes for correctness and flags issues early, so bad deployments don’t reach the warehouse. 3) How it stands out: Unlike tools that mainly detect problems after they happen, Datafold focuses on preventing issues by integrating testing into the development process. It targets data teams across industries and uses a subscription model for ongoing access. 4) The goal: To improve data integrity and development speed by stopping data quality problems before they reach production data warehouses.

Company size
11-50
Company stage
Early VC
Total funding
$26.2M
Headquarters
New York City, New York
Founded
2020

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