$70-$190/hr data engineering work, on your schedule
Review AI pipelines and warehouse models for the silent data loss, the broken idempotency, the model that fans out wrong. The judgment that keeps data trustworthy at scale. Paid hourly, remote, a few hours a week.
Trusted by top research companies
Hi, we're Zac and Jack, the founders of Terac. We want to talk to you directly, because you are the most important part of what we're building.
Terac is a community of experts. People who have spent years getting good at something specific and hard. The world is about to need more of you, not less. As AI takes on more of the world's work, the bottleneck shifts to the people who actually know what they're talking about.
Expert labor is the rarest resource in the world right now, and it is shockingly hard to find. The companies that need a data engineer's eye on a pipeline that silently drops rows spend weeks chasing people, paying placement fees, and settling for whoever is available. Meanwhile thousands of qualified people are sitting with knowledge that no one ever asks for.
That gap is what we're here to close. Every project that lands on Terac is routed to the people who actually know the answer, on their schedule, paid fairly, and only when the work is verified. No middleman taking a cut of your time. No vague gigs. No chasing checks.
We care about every single person in this community. If you join Terac, you're not a row in a database to us. We read the feedback. We answer the emails. We will fight for you when a customer is being unreasonable, and we will be honest with you when something on our side is broken. The quality of this panel is our entire company, and we owe you a serious bar.
If you've made it this far, here is what we're asking: claim your profile. Put your expertise on the record. Let the world's most ambitious teams come find you for the work only you can do.
Data Engineering questions
Still curious? Write to us at support@terac.com.
Narrow depth is exactly what is most valuable. Models struggle most with opinionated, tool-specific reasoning, so a deep dbt practitioner explaining incremental materialization tradeoffs, or an Iceberg expert spotting a bad partition spec, produces higher-signal data than a generalist. You are matched to tasks that fit your actual stack.
It varies, but commonly reviewing AI SQL transformations and dbt models for correctness and style, evaluating Airflow or Prefect orchestration code, assessing data quality rules, and writing worked examples of schema design or pipeline debugging. You see a task description before committing, so you can decline anything outside your comfort zone.
Certs like the Databricks Data Engineer tiers or the dbt Analytics Engineer cert are one signal during verification to calibrate seniority and tool familiarity, not a hard gate. Equivalent experience counts just as much. They mainly help route you toward Spark, Delta Lake, or lakehouse tasks where that background produces more reliable evaluations.
No task requires you to use, reference, or reproduce proprietary assets from your employer. Every scenario is fully synthetic or based on anonymized, publicly available schemas and datasets. Your job is to assess the AI's reasoning and correctness with your own expertise, not to expose anything from your current role.
It helps for a specific subset of work. Tasks asking models to reason about data lineage, audit logging, PII handling, or access control in regulated environments need reviewers who know what correct looks like there. Your HIPAA or SOX experience is noted on your profile and used to route those evaluations to you.
Why your expertise matters
A model writes Spark, dbt, or Airflow that looks fine and is subtly broken: a bad partition strategy, faulty incremental load logic, a schema choice that silently corrupts downstream models. A generalist cannot see it. Knowing whether a pipeline performs at scale, handles schema drift, or respects concurrency limits is your judgment, and it makes AI-built data infra trustworthy.
How pay works
The $190 ceiling goes to high-demand depth: cloud warehousing on Snowflake, BigQuery, or Redshift paired with Airflow or Dagster orchestration, or streaming on Kafka and Flink. Work is remote, billed by the verified hour, and paid only after your deliverable passes the completion check. No unpaid hours, no end-of-project disputes.
What the work looks like
A sample of the data engineering work you would pick up. Every project is scoped, remote, and paid on verified completion.
- Review a model's dbt model for correct incremental materialization and flag grain mismatches that cause silent fan-out downstream.
- Evaluate an AI Spark job for shuffle-heavy joins, missing partition pruning, and executor memory settings that fail at production scale.
- Assess a model's Airflow DAG for bad dependency chaining, missing SLAs, and catchup behavior that causes duplicate loads.
- Write a worked example of a correct CDC pattern using Debezium and Kafka into an Iceberg table, annotated to show how each choice avoids data loss during schema evolution.
- Score AI Snowflake SQL on clustering key alignment, transient table use, and unnecessary cross-database joins.
- Judge whether a model's data vault implementation correctly separates hubs, links, and satellites and survives a source system migration.
Specialties we match
Data Engineering projects span a wide range of focus areas. Tell us where you go deep and we route the work that fits.
- Apache Spark / PySpark
- dbt (data build tool)
- Apache Airflow / Dagster
- Snowflake / BigQuery / Redshift
- Apache Kafka / Flink
- Delta Lake / Apache Iceberg
- Data vault modeling
- ELT/ETL pipeline design
- Stream processing
- Data quality frameworks (Great Expectations, Soda)
- CDC (Change Data Capture)
- Column-level lineage
