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.