Hire a data engineer with A.Team

The typical data engineer placed on a mission has eight to twelve years of production experience building data pipelines and data platforms at comparable companies. The bar is for engineers who've owned a data system in production: managing schema migrations, handling backpressure, and designing for downstream consumer needs. Contributors who've added columns to existing schemas are below the bar.

Two to three matched data engineers land on your desk within 72 hours of an intake submission at a.team/get-started. Standard Python/Snowflake engagements land fastest; PySpark/Databricks and AI data infrastructure engagements run three to five business days.

A.Team's data engineering network includes engineers with AI data pipeline experience, feature store design, embedding pipeline management, and real-time data streams for ML inference. The intake brief should specify whether the scope is traditional ETL/warehouse work or AI-adjacent data infrastructure so the matching returns engineers with the right production history.

Every engagement includes a trial period at the front end. If the match needs to shift, your Team Success contact adjusts the team without restarting the commercial.

A data engineer builds and manages the infrastructure that moves, stores, and transforms data. A data scientist analyzes data, runs experiments, and builds statistical or ML models. If the primary scope is pipeline reliability, data quality, or warehouse architecture, hire a data engineer. If the primary scope is analysis, model development, or insight generation from the data that already exists, hire a data scientist.

US-based senior data engineers working as contractors typically run $130 to $200 per hour. PySpark/Databricks specialists and AI data infrastructure engineers are at the higher end. Full benchmarks are in the data engineer rate guide.

An FTE data engineer search takes 60 to 90 days in most markets. A contractor through a curated platform takes one to three weeks. A.Team's intake-to-working-builder timeline typically runs 72 hours for single-engineer engagements and up to two weeks for data team builds.

A backend engineer owns API layers, service logic, and application databases, the systems that serve user-facing features. A data engineer owns the pipelines, warehouses, and streaming infrastructure that move and transform data at scale, typically not user-facing but powering the analytics, reporting, and AI systems that sit above it. For work that spans both (a microservice that also processes data at scale), a data engineer with strong backend skills is the right frame.

A.Team matches on the stack you specify. The most common data engineering stacks in the mission set are Python + Snowflake, Python + Databricks/PySpark, and Python + BigQuery. For AI-adjacent data engineering, the stack often adds Pinecone (vector database) or Redis (real-time caching). The hiring guide has a stack selection framework for companies choosing a data platform.