Data pipeline, analytics engineering and data platform roles from AI and data companies — listed from their own career pages.
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Data pipeline, analytics engineering and data platform roles. Updated daily.
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Posted 6 days ago
At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are en...
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Posted 6 days ago
Why HarveyAt Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, a...
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Posted 6 days ago
Why HarveyAt Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, a...
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Posted 6 days ago
Why HarveyAt Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, a...
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Posted 6 days ago
Perplexity is AI for people who expect more. On the data team, that means building the systems that make our data reliable, understandable, and usable...
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Posted 6 days ago
About AlphaSense: The world’s most sophisticated companies rely on AlphaSense to remove uncertainty from decision-making. With market intelligence an...
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Posted 6 days ago
Shield AI is a venture-backed defense-tech company with the mission of protecting service members and civilians with intelligent systems. Its products...
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Posted 1 week ago
Agility’s commercially deployed humanoids operate alongside teams in warehouses, manufacturing facilities, and distribution centers—tackling physicall...
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Posted 3 weeks ago
Who We AreNotion is the collaborative AI workspace where teams and agents think together. We're building one place where your knowledge, projects, mee...
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Posted 3 weeks ago
This role is FULLY REMOTE from Latin America - Pacific Time zoneHey, we're Glacier! A Series A startup based in San Francisco tackling one of the worl...
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Career context
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
Every company on RemoteAI is actively hiring AI talent. No noise, no ghost listings, no recruiter markups.
Every role on RemoteAI is an AI specialist position. No noise, no irrelevant listings from unrelated industries.
No recruiter in the middle. Your application goes straight to the company making the decision. Faster responses, no markups.
Creating a profile, applying, and getting hired costs nothing. No placement fee, no revenue percentage, no exclusive contracts.
Every listing shows compensation range before you apply. No wasted interviews with companies outside your range.
Every role is remote. Some require timezone overlap — the listing will specify. Filter by region if that matters to you.
Track all your applications, messages, and interviews in one place. No more losing track of where you applied.
Step by step
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.
Volume, sources, freshness, failure handling. "Ingests 2 TB/day from 40 sources with 15-minute freshness" says more than a tool list.
Tests, contracts, monitoring and alerting. Teams hire data engineers who prevent bad data, not only move it.
Each listing links to the official application. Mention the warehouse and orchestration tools you match.
Profile guide
Not all profiles get responses. These are the signals that separate candidates who get interviews from those who don't.
Dimensional models, dbt, clear and fast SQL — the base of every data engineering interview.
Airflow, Dagster, Prefect: scheduling, retries, backfills and dependencies done right.
Kafka, Flink or Spark Streaming experience is rarer and better paid.
Snowflake, BigQuery, Databricks, Redshift — including cost control.
Feature pipelines, training datasets, embeddings and vector stores connect data engineering to AI teams.
SLAs, lineage, incident handling. Pipelines that run quietly for months are the goal.
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FAQ
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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