Hire Data Engineers

Ideaware places senior data engineers from Latin America on US data and product teams. You see vetted candidates within 48 hours, your pick starts 8–12 days after the first call, and they work US hours alongside your analysts. A senior data engineer costs $5,500–9,000 a month, with payroll, benefits and equipment included.

A data engineer builds the pipelines, warehouse models and quality checks that your dashboards and ML models depend on. We look for engineers who write that work as tested, version-controlled code, in dbt and Python, and who have run it in production.

What a data engineer costs in the US

A US data engineer earns an average base salary of $125,983 in 2026, and $150,234 in total compensation, according to Built In’s salary data. The BLS median for all software developers was $135,980 in May 2025.

Benefits come on top: they make up 30% of employer compensation costs in US private industry (BLS, June 2026). A recruiter fee adds more.

	US in-house hire	Ideaware
Pay	$125,983 average base salary	$5,500–9,000 a month
Benefits, payroll tax, equipment	Paid by you on top of salary	Included
Placement fee	Common with agencies	None
First candidates	Depends on your pipeline	48 hours

Teams save 40–60% against a comparable US hire once benefits and fees count. See the developer cost guide for every role.

The data stack our engineers work in

Our data engineers write advanced SQL and Python every day and build on cloud warehouses rather than legacy ETL tools. Typical profiles cover:

Warehouses and lakehouses: Snowflake, BigQuery, Redshift, Databricks with Delta Lake
Transformation: dbt models, tests and documentation; SQL window functions; PySpark for large jobs
Orchestration: Airflow, Dagster or Prefect, with retries, backfills and SLAs
Ingestion: Fivetran, Airbyte, custom API connectors, change data capture with Debezium
Streaming: Kafka, Kinesis, Pub/Sub, Flink or Spark Structured Streaming
Quality and governance: dbt tests, Great Expectations or Soda, data contracts, column-level access controls
Cloud: AWS (S3, Glue, EMR, Lambda), GCP (Dataflow, Cloud Storage), Terraform for the infrastructure

When the data feeds a model, a data engineer usually works next to an ML engineer or an AI engineer.

How we vet data engineers

We accept about 3% of applicants. Data engineering candidates pass five steps: an async technical assessment, a 90-minute live coding session, an architecture review of a pipeline they built, an English and communication interview, and reference checks with former managers.

The technical rounds test the parts that fail without anyone noticing:

Modeling: a dimensional model for a messy source, slowly changing dimensions, and grain decisions
SQL: window functions, deduplication and incremental loads that stay correct on late data
Pipeline design: idempotent jobs, backfills and what happens when an upstream API changes shape
Data quality: where to put tests, how to alert on freshness and volume, and who owns a broken metric
Warehouse cost and speed: partitioning, clustering and reading a query plan in Snowflake or BigQuery
Streaming trade-offs: when a batch job every 15 minutes beats a Kafka pipeline

Data engineer, data scientist or ML engineer: which hire do you need?

Hire a data engineer first when your data is scattered, untrusted or slow to query. Data scientists and analysts depend on that foundation. A different profile fits when:

You need models trained and deployed: hire an ML engineer.
You want LLM features built on your data: hire an AI engineer.
You need Python services and APIs more than pipelines: hire a Python developer.
Your pipelines run on AWS and the infrastructure needs an owner: add an AWS developer.

Frequently asked questions

How much does it cost to hire a data engineer?

A senior data engineer through Ideaware costs $5,500–9,000 a month, all-in. US data engineers average $125,983 in base salary in 2026 (Built In), before benefits and recruiting fees. The cost guide covers other roles.

How fast can a data engineer start?

You see vetted profiles within 48 hours. Most engineers start 8–12 days after the first call, depending on how quickly you interview.

Do your data engineers know dbt, Snowflake and Airflow?

Yes. That combination is the most common stack we screen for. Tell us your warehouse and orchestrator and we send candidates who have run them in production, not only in a course.

Do we need a data engineer or an analytics engineer?

An analytics engineer lives in SQL and dbt and shapes data for BI tools. A data engineer also owns ingestion, orchestration, streaming and the infrastructure. If you have no pipelines yet, start with a data engineer.

Can a data engineer migrate us off a legacy ETL tool?

Yes. Ask for candidates who have moved Informatica, SSIS or hand-written cron jobs into dbt and an orchestrator. The usual plan runs old and new pipelines side by side and compares outputs before switching.

What happens if the engineer isn't a fit?

We replace them at no cost within the first 90 days. After that you can scale down with 30 days’ notice and no placement fees.

Get data engineer candidates in 48 hours →