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About This Role
Job description
We are looking for a Senior Data Engineer to join a multidisciplinary team focused on healthcare data engineering, analytics, and AI\-driven solutions. In this role, you will work with large\-scale physiological, medical device, and electronic medical record (EMR) datasets to support data quality, predictive modeling, and advanced analytics initiatives.
You will collaborate with data scientists, software engineers, biomedical specialists, and IT teams to ensure reliable data pipelines, investigate data integrity issues, and contribute to the development and validation of machine learning models.
Location
Cambridge, Massachusetts, USA (On\-site)
Key Responsibilities
Retrieve, explore, and analyze data stored in Amazon S3 using AWS Athena and Amazon SageMaker.
Perform data wrangling by identifying missing data, time synchronization issues, anomalies, and measurement artifacts.
Transform and parse CSV and XML datasets into scalable databases such as InfluxDB.
Develop and maintain batch processing workflows using Python and Bash to inventory, process, and validate incoming device data.
Investigate data quality issues through Athena, SageMaker, and Jupyter Notebooks to identify problems related to data curation, system configuration, storage, or performance.
Troubleshoot data transfer and collection issues in collaboration with biomedical teams, IT stakeholders, and internal engineering teams.
Analyze physiological, vital sign, and EMR datasets to support predictive analytics and AI/ML model development.
Validate statistical and machine learning outputs by comparing Python\-generated results with SAS analyses produced by biostatistics teams.
Simulate retrospective healthcare datasets using a Virtual Hospital Simulator to evaluate and test surveillance algorithms.
Contribute to continuous improvements in data engineering processes, data quality, and analytical workflows.
Required Qualifications
7–10 years of experience in Data Engineering, Data Analytics, or a related technical field.
Strong proficiency in Python for data processing and automation.
Experience with AWS services, particularly Amazon S3, Athena, and SageMaker.
Experience working with Jupyter Notebooks.
Solid understanding of ETL processes, data transformation, and data validation.
Experience handling structured and semi\-structured data formats, including CSV and XML.
Familiarity with batch scripting using Bash.
Strong analytical and troubleshooting skills with a focus on data quality and integrity.
Experience working with SQL and large datasets.
Excellent communication and collaboration skills in cross\-functional environments.
Nice to Have
Experience with InfluxDB or other time\-series databases.
Knowledge of AI/ML workflows and predictive analytics.
Experience working with physiological, medical device, or EMR data.
Familiarity with SAS and statistical validation processes.
Experience in the healthcare, medical technology, or life sciences industry.
Exposure to biomedical data analysis or clinical data environments.
Why choose us
An international community bringing together more than 110 different nationalities
An environment where trust is central: 70% of our leaders started their careers at the entry level
A strong training system with our internal Academy and more than 250 modules available
A dynamic work environment that frequently comes together for internal events (afterworks, team buildings, etc.)
Amaris Consulting promotes equal opportunities. We are committed to bringing together people from diverse backgrounds and creating an inclusive work environment. In this regard, we welcome applications from all qualified individuals, regardless of sex, sexual orientation, race, ethnicity, beliefs, age, marital status, disability, or other characteristics.
Who are we?
Amaris Consulting is an independent technology consulting firm providing guidance and solutions to businesses. With more than 1000 clients across the globe, we have been rolling out solutions in major projects for over a decade – this is made possible by an international team of 7,600 people spread across 5 continents and more than 60 countries. Our solutions focus on four different Business Lines: Information System \& Digital, Telecom, Life Sciences and Engineering. We’re focused on building and nurturing a top talent community where all our team members can achieve their full potential. Amaris is your steppingstone to cross rivers of change, meet challenges and achieve all your projects with success.
At Amaris, we strive to provide our candidates with the best possible recruitment experience. We like to get to know our candidates, challenge them, and be able to give them proper feedback as quickly as possible. Here's what our recruitment process looks like:
Brief Call: Our process typically begins with a brief virtual/phone conversation to get to know you! The objective? Learn about you, understand your motivations, and make sure we have the right job for you!
Interviews (the average number of interviews is 3 \- the number may vary depending on the level of seniority required for the position). During the interviews, you will meet people from our team: your line manager of course, but also other people related to your future role. We will talk in depth about you, your experience, and skills, but also about the position and what will be expected of you. Of course, you will also get to know Amaris: our culture, our roots, our teams, and your career opportunities!
Case study: Depending on the position, we may ask you to take a test. This could be a role play, a technical assessment, a problem\-solving scenario, etc.
As you know, every person is different and so is every role in a company. That is why we have to adapt accordingly, and the process may differ slightly at times. However, please know that we always put ourselves in the candidate's shoes to ensure they have the best possible experience.
We look forward to meeting you!
Role Details
About This Role
Data Engineers build the pipelines that feed AI models. They design ETL workflows, manage data lakes, and ensure training and inference data is clean, timely, and accessible. Without good data engineering, AI projects fail. It's that simple.
The AI era has expanded the data engineer's scope far beyond batch ETL jobs. You're building real-time embedding pipelines for RAG systems, managing vector databases, ensuring training data quality at scale, and building the infrastructure that lets ML teams iterate on data as fast as they iterate on models. Data quality is the biggest predictor of model quality, and you're the person responsible for it.
Across the 3,708 AI roles we're tracking, Data Engineer positions make up 1% of the market. At Amaris Consulting, this role fits into their broader AI and engineering organization.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
What the Work Looks Like
A typical week includes: debugging a data pipeline that's producing stale embeddings for the RAG system, optimizing a Spark job that processes training data, building a data quality monitoring dashboard, meeting with the ML team to understand their next data requirements, and writing dbt models that transform raw event data into ML-ready features. The work is deeply technical and high-impact.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
Skills Required
SQL, Python, and distributed systems (Spark, Airflow, dbt) are core. Cloud data platforms (Snowflake, BigQuery, Redshift) are increasingly standard. Many AI-focused roles also want familiarity with vector databases and embedding pipelines. Understanding data modeling, pipeline orchestration, and data quality frameworks covers the essentials.
AI-specific data engineering skills include: building feature stores, managing training data versioning, implementing data lineage tracking, and building real-time embedding pipelines. Experience with streaming systems (Kafka, Flink) is valuable for real-time AI applications. Understanding ML data requirements (balanced datasets, data augmentation, evaluation set construction) makes you much more effective working with ML teams.
Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
Compensation Benchmarks
Data Engineer roles pay a median of $178,800 based on 40 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Amaris Consulting AI Hiring
Amaris Consulting has 1 open AI role right now. They're hiring across Data Engineer. Based in Cambridge, MA, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into Data Engineer roles include Backend Engineer, Database Administrator, Analytics Engineer.
From here, career progression typically leads toward Senior Data Engineer, ML Engineer, Data Platform Lead.
Master SQL and Python first. Then learn a distributed processing framework (Spark or its modern alternatives) and a pipeline orchestrator (Airflow, Dagster, Prefect). Build a portfolio project that demonstrates end-to-end pipeline construction: ingest, transform, validate, serve. If you want to specialize in AI data engineering, add vector databases and embedding pipelines to your skill set.
What to Expect in Interviews
Expect SQL deep-dives (query optimization, partitioning strategies, data modeling), Python coding focused on data pipeline patterns, and system design questions about building scalable ETL workflows. Companies with ML teams will ask about feature stores, embedding pipelines, and training data management. Be ready to discuss data quality monitoring, pipeline orchestration, and how you'd handle schema evolution in a production data lake.
When evaluating opportunities: Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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