Posted directly on LandEarly · Sep 13

Data Engineer

ABA RankPosted Sep 13, 2026
Data EngineerFull TimeRemoteMid
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Sep 13
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Job descriptionFull Time

ABA Rank operates an independent, publicly ranked directory of Applied Behavior Analysis clinics, software vendors, and service providers, with an index that recomputes nightly across thousands of listings. We're looking for a Data Engineer to help build and scale the data infrastructure that powers our ranking engine, provider index, and public-facing tools (search, filters, API, and MCP server).

What you'll do

  • Design, build, and maintain data pipelines that ingest, clean, and normalize provider, review, and listing data at scale
  • Support the nightly recompute of the ranking Index, ensuring accuracy, reliability, and timely delivery
  • Build and maintain the infrastructure behind the public JSON API, structured data feeds, and MCP server
  • Monitor data quality and pipeline health, and build alerting and validation to catch issues before they reach production
  • Collaborate with product and engineering to expose ranked, structured data through search, filters, and the buyer-matching tools
  • Optimize data storage, schemas, and query performance as the index and listing volume grow

What we're looking for

  • Professional experience building and operating data pipelines in a production environment
  • Strong SQL skills and experience with at least one modern data pipeline or orchestration tool (e.g., Airflow, dbt, or similar)
  • Experience with a general-purpose programming language commonly used in data engineering (e.g., Python)
  • Familiarity with relational databases and data modeling for analytical and operational workloads
  • Experience working with APIs and structured data formats (JSON, schema.org, or similar)
  • Comfort working independently and communicating clearly about data tradeoffs and pipeline design

Nice to have

  • Experience with search/ranking systems or recommendation-style scoring pipelines
  • Experience publishing data for machine consumption (structured data, public APIs, or LLM-facing formats)
  • Familiarity with nightly batch processing at scale

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