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Data pipeline, analytics engineering and data platform roles from AI and data companies — listed from their own career pages.
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Posted 2 hours ago
Data Scientist/Data Engineer (Mid to Sr Levels)
About us:Nomic was founded with a simple but ambitious goal: to make biology easier to measure. We’ve developed nELISA, the world’s highest throughput...
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Posted 8 hours ago
Product Analytics Engineer
The RoleAs a Product Analytics Engineer at Nous Research, you'll own the measurement systems that help us understand how users interact with Hermes Ag...
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Posted 10 hours ago
Senior Data Engineer
About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that suppo...
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Posted 12 hours ago
Software Engineering Manager -Data Platform
Mission Summary:We are seeking an experienced and visionary Staff Level Technical Lead Manager to build and lead our new Data Platform team. This pivo...
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Posted 17 hours ago
Senior Software Engineer, Data Platform
Mission Summary: The Data Platform team is the vital link between our company's expansive data and the engineering teams that rely on it. We architect...
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Posted 20 hours ago
Analytics Engineer
Metabase is the easiest way for people to get insights from their data, from tiny startups who get up and running quickly to major corporations with t...
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Posted 21 hours ago
Senior Data Engineer
About EveEve is redefining legal technology for plaintiff law firms, and we're building the team that will take us there. We help firms handle more c...
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Posted 1 day ago
Data Engineer
Data Engineer Prolific Prolific is not just another player in the AI space—we are the architects of the human data infrastructure that's resha...
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Posted 2 days ago
Senior Analytics Engineer - Decision Intelligence
Company OverviewDeepgram is the leading platform underpinning the emerging trillion-dollar Voice AI economy, providing real-time APIs for speech-to-te...
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Posted 2 days ago
Data Engineer, App SW
Before the detail, here's the challenge you'd help us solve.We build the embodied intelligence that moves real vehicles safely, and the ecosystem a bi...
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Career context
Every AI system starts with a data pipeline
Data engineers build and run the systems that move, clean and model data: ingestion, ETL and ELT pipelines, warehouses and lakehouses, streaming, and the quality checks that make analytics and machine learning trustworthy. AI has made the role more important, not less — models are only as good as the data that reaches them.
The roles below come from data infrastructure companies, AI labs and product teams. They range from analytics engineering (SQL, dbt, modelling for analysts) to platform work (Spark, Kafka, orchestration, cost and reliability at scale). Read each listing to see which side it leans to.
Why RemoteAI
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Every company on RemoteAI is actively hiring AI talent. No noise, no ghost listings, no recruiter markups.
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Every role on RemoteAI is an AI specialist position. No noise, no irrelevant listings from unrelated industries.
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Creating a profile, applying, and getting hired costs nothing. No placement fee, no revenue percentage, no exclusive contracts.
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Every listing shows compensation range before you apply. No wasted interviews with companies outside your range.
Fully remote positions
Every role is remote. Some require timezone overlap — the listing will specify. Filter by region if that matters to you.
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Step by step
How to find a data engineer job on RemoteAI
Data engineering pays close to software engineering, with a premium for streaming, platform and ML-data experience. Each listing shows the range the employer published.
Describe pipelines you own
Volume, sources, freshness, failure handling. "Ingests 2 TB/day from 40 sources with 15-minute freshness" says more than a tool list.
Show data quality habits
Tests, contracts, monitoring and alerting. Teams hire data engineers who prevent bad data, not only move it.
Apply on the employer's page
Each listing links to the official application. Mention the warehouse and orchestration tools you match.
Profile guide
How to stand out as a data engineer
Not all profiles get responses. These are the signals that separate candidates who get interviews from those who don't.
SQL and data modelling
Dimensional models, dbt, clear and fast SQL — the base of every data engineering interview.
Orchestration
Airflow, Dagster, Prefect: scheduling, retries, backfills and dependencies done right.
Streaming
Kafka, Flink or Spark Streaming experience is rarer and better paid.
Cloud warehouses
Snowflake, BigQuery, Databricks, Redshift — including cost control.
Data for ML
Feature pipelines, training datasets, embeddings and vector stores connect data engineering to AI teams.
Reliability
SLAs, lineage, incident handling. Pipelines that run quietly for months are the goal.
Explore more roles
Every AI specialty. One platform.
ML Engineer jobs
Production ML, deep learning & AI infrastructure
LLM Engineer jobs
RAG pipelines, agentic systems & generative AI
Data Scientist jobs
Analytics, forecasting & applied ML
MLOps Engineer jobs
ML infrastructure, model serving & CI/CD
NLP Engineer jobs
Language models, transformers & text AI
AI Product Manager jobs
AI-native product strategy & roadmap
Computer Vision Engineer jobs
Image AI, object detection & edge deployment
AI Automation Specialist jobs
AI agents, n8n & workflow automation
Prompt Engineer jobs
LLM evaluation, RAG & prompt optimization
AI Trainer jobs
AI tutoring, RLHF & red teaming
Data Annotation jobs
Labeling, rating, evaluation & transcription
AI Engineer jobs
Applied AI, LLM & generative AI products
Forward Deployed Engineer jobs
Ship AI solutions with customers
AI Safety & Governance jobs
Alignment, evaluations & responsible AI
Entry-Level AI Jobs jobs
Junior roles & AI internships
Freelance AI Jobs jobs
Contract & freelance AI projects
FAQ
Questions about remote data engineer jobs
Everything you need to know before applying for your first data engineer role.
A data engineer builds the systems that collect, move, clean and store data so analysts, products and machine learning models can use it — pipelines, warehouses, streaming and data quality checks.
Analytics engineers focus on modelling data inside the warehouse for analysis, mostly with SQL and dbt. Data engineers usually own ingestion, infrastructure and platform reliability. Many listings mix both.
SQL and Python first, then a cloud warehouse (Snowflake, BigQuery, Databricks), an orchestrator (Airflow, Dagster), dbt, and for platform roles Spark or Kafka.
Yes. AI teams depend on training data, feature pipelines and retrieval data. Data engineers who understand ML data needs move naturally into ML platform and AI infrastructure roles.
Yes. Profiles, browsing and applying are free, with no cut of your salary.
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