Data Engineer | Remote | $180,000/year
Overview
micro1 is a remote-first company, and this data engineer role exists to support AI product teams and internal research. The work involves creating distributed data pipelines, managing high-volume datasets on AWS, and making sure storage works well for both SQL and NoSQL systems. Expect to write a substantial amount of Python and SQL, run large jobs on Spark, and tune partition strategies to keep processing fast and cost controlled. Data scientists and AI researchers rely on the data infrastructure you maintain, so clear communication on data quality and pipeline health matters.
What You'll Do8
- 1Build and own data pipelines that bring raw information from multiple sources into a single, usable layer.
- 2Run and tune Spark processing jobs on AWS so large data transforms stay within time and budget limits.
- 3Design storage layouts that serve both SQL and NoSQL databases, balancing read speed, write volume, and cost.
- 4Architect AWS data flows for ingestion, processing, and distribution so nothing depends on fragile manual handoffs.
- 5Write Python and SQL routines for extraction, cleaning, validation, and feature generation that researchers can trust.
- 6Set up data quality checks, monitoring, and automated alerts that catch problems before they reach downstream consumers.
- 7Work with AI researchers and data scientists to provide clean, well-shaped datasets for model training and evaluation.
- 8Automate orchestration and recovery tasks so pipeline reruns do not require someone watching logs all day.
Requirements7
- 1Strong Python and SQL skills, shown through production code that other engineers can read and modify.
- 2Proven hands-on experience with Apache Spark in a distributed environment.
- 3Working knowledge of AWS data services such as S3, Glue, Redshift, or EMR, and the ability to choose the right one.
- 4Comfort designing schemas and query patterns for both relational databases and NoSQL stores.
- 5Experience handling datasets that do not fit on a single machine, including smart partitioning and memory management.
- 6A solid instinct for data quality, operational monitoring, and pipeline reliability.
- 7Familiarity with AI/ML research workflows, LLM dataset creation, or data visualization tools like Matplotlib, Seaborn, or Plotly is a plus.
Who Should Apply
The engineer who will thrive here treats data infrastructure as a product, not just a script to move records around. If you have spent real time on AWS, Spark, and both relational and NoSQL databases, and you enjoy collaborating with researchers whose data needs evolve quickly, this will feel like a good match. The role is not for people who prefer to stay within dashboarding or pure analytics, and it is also wrong for engineers who expect a separate DevOps person to handle reliability. Applicants often lose out when their Spark experience is limited to toy projects or they cannot talk about how they partitioned data at scale. A second pattern that hurts candidates is listing AWS on paper but not being able to explain why they chose a certain service for a specific workload.
Salary Insight
For full-time employment, micro1 lists the base salary range as $100,000 to $150,000 per year, with the final figure based on experience, location, and role level. Employees also receive equity and can qualify for performance-based bonuses, which depend on company policy. The benefits package includes full reimbursement of health-insurance premiums (up to 100%), paid time off, a 401(k) with company match, and other remote-work support programs.
Location
Required Skills
Application Tip
Before you apply, build a one-page summary of a recent data pipeline project where you used Spark and AWS. Be precise about dataset size, partitioning approach, latency improvements, and how you coordinated with data scientists or AI researchers. Mention any LLM or experimentation data work you have done, since that is a frequent differentiator for this role.
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