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Data teams build datasets, pipelines, and dashboards — but they rarely measure whether anyone actually uses them. A pipeline that runs daily and serve...
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API pricing is one of the least understood levers in SaaS. Should you charge per call, per token, per outcome, or per seat? The answer depends on usag...
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We already have the data. Three years of clickstream events, purchase histories, and customer support interactions from our e-commerce platform — all...
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Our healthcare clients want to train ML models but can't share patient data. HIPAA makes real medical datasets a compliance nightmare. The solution: s...
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Government agencies publish vast amounts of data — court records, permit filings, inspection reports, budget documents — but each one in a different f...
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Testing with production data is a liability lawsuit waiting to happen. Our synthetic data platform generates realistic test datasets that preserve the...
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Every ML model is only as good as its data, and ours processes 800GB daily from IoT sensors deployed across 200 wind farms. Turbine vibration data, we...
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Models are only as good as their training data, and our training data comes from 200 annotators working across three time zones producing 15,000 label...
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Genomic datasets are massive, messy, and growing exponentially. A single whole-genome sequencing run produces 200GB of raw data, and we process 400 ru...
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We're a 45-person company and our data infrastructure has outgrown the scripts-and-cron-jobs phase. Our ML team needs reliable, tested, documented dat...
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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
Built for AI specialists.
Not general job boards.
Every company on RemoteAI is actively hiring AI talent. No noise, no ghost listings, no recruiter markups.
AI-only job listings
Every role on RemoteAI is an AI specialist position. No noise, no irrelevant listings from unrelated industries.
Direct access to hiring teams
No recruiter in the middle. Your application goes straight to the company making the decision. Faster responses, no markups.
Always free for candidates
Creating a profile, applying, and getting hired costs nothing. No placement fee, no revenue percentage, no exclusive contracts.
Salary shown upfront
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.
Full application tracking
Track all your applications, messages, and interviews in one place. No more losing track of where you applied.
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.
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Analytics, forecasting & applied ML
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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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