Data Engineer – AI-First Staffing Platform

Qualifications

• 3–7 years of experience as a Data Engineer or related backend-engineering role focused on data systems
• Exposure to or interest in MLOps-model deployment, feature pipelines, and reproducible training environments
• Hands-on experience with AWS data technologies (S3, Glue, Lambda, Redshift, Athena, Step Functions, or similar)
• Familiarity with data-orchestration tools such as Airflow, Prefect, or Dagster
• Understanding of data modeling, storage optimization, and schema design for analytics or ML workloads
• Experience with operational monitoring of data environments and workflows

Responsibilities

• You'll design production data pipelines, automate manual workflows, and work closely with software and data science teams to make machine-learning models production-ready
• This hands-on role combines immediate impact with long-term growth: you'll deepen your expertise in modern data infrastructure while gaining experience in MLOps, AI data pipelines, and infrastructure-as-code on AWS – all in a supportive, learning-oriented environment that values curiosity and shared success
• Design, build, and maintain scalable ETL and data pipelines in Python on AWS
• Automate manual data workflows to improve repeatability and speed
• Monitor and optimize pipelines for reliability, latency, and data quality
• Create code, systems, and policies for handling sensitive/PII information at scale
• Develop and maintain data environments with infrastructure-as-code on AWS
• Contribute to observability and performance monitoring – defining metrics and dashboards that highlight system health
• Partner with data scientists and ML engineers to develop data flows powering model training, evaluation, and inference
• Collaborate in reviews and design discussions to help set standards for data-engineering best practices
• Document schemas, transformations, and data lineage for transparency and maintainability

Full Description

Location: Boston (Hybrid)

Reports to: Head of Engineering

Type: Full-time

About AdeptID

AdeptID is building an AI-first staffing company.

Over five years, we've built a talent-matching engine that powers 10 million job recommendations every month for major HR technology platforms.

We're now using that technology to support an end-to-end staffing firm for the healthcare sector – focused on solving healthcare talent shortages with AI.

We're backed by top investors and poised to 10x our footprint by 2027.

Role Overview

We're seeking a mid-level or senior Data Engineer to build and evolve the data backbone of AdeptID's AI-first staffing platform.

Our systems process millions of talent and job records monthly, ensuring that data is reliable, high-quality, and secure.

You'll design production data pipelines, automate manual workflows, and work closely with software and data science teams to make machine-learning models production-ready.

This hands-on role combines immediate impact with long-term growth: you'll deepen your expertise in modern data infrastructure while gaining experience in MLOps, AI data pipelines, and infrastructure-as-code on AWS – all in a supportive, learning-oriented environment that values curiosity and shared success.

If you're motivated by solving data challenges at scale and excited to build and learn about high-performing AI systems, you'll thrive here.

Responsibilities

• Design, build, and maintain scalable ETL and data pipelines in Python on AWS

• Automate manual data workflows to improve repeatability and speed

• Monitor and optimize pipelines for reliability, latency, and data quality

• Create code, systems, and policies for handling sensitive/PII information at scale

• Develop and maintain data environments with infrastructure-as-code on AWS

• Contribute to observability and performance monitoring – defining metrics and dashboards that highlight system health

• Partner with data scientists and ML engineers to develop data flows powering model training, evaluation, and inference

• Collaborate in reviews and design discussions to help set standards for data-engineering best practices

• Document schemas, transformations, and data lineage for transparency and maintainability

Qualifications

• 3–7 years of experience as a Data Engineer or related backend-engineering role focused on data systems

• Exposure to or interest in MLOps-model deployment, feature pipelines, and reproducible training environments

• Hands-on experience with AWS data technologies (S3, Glue, Lambda, Redshift, Athena, Step Functions, or similar)

• Familiarity with data-orchestration tools such as Airflow, Prefect, or Dagster

• Understanding of data modeling, storage optimization, and schema design for analytics or ML workloads

• Experience with operational monitoring of data environments and workflows

Next Steps

Well-qualified candidates will be invited to submit short responses to a set of screening questions and meet with a member of the hiring team.