Background

Hire remote data engineers that you can trust

Hire expert data engineers from a 50,000+ network of pre-vetted talents. Boundev engineers excel at building scalable ETL pipelines, data warehouses, and analytics infrastructure, with zero overhead for your company.

Why hire data engineers with Boundev

48-hour data expert matching

48-hour data expert matching

We handpick data engineers skilled in Spark, Airflow, Snowflake, and modern data stacks.

Zero overhead

Zero overhead

Locally-compliant contracts and billing with no hidden fees or administrative burden.

Dedicated data support

Dedicated data support

High-priority service ensuring seamless data pipeline development and optimization.

Rated #1 among hiring platforms for

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How to hire data engineers with Boundev

1

Share your data requirements

Set up a quick call with one of our Matching Experts to discuss your data infrastructure needs—ETL pipelines, data warehousing, real-time analytics, or big data processing.

2

Personalized data talent matching

Our experts curate a list of data engineers with expertise in your tech stack—Spark, Airflow, dbt, Snowflake, Databricks, or custom data solutions.

3

Interview pre-vetted candidates

Review data engineers who have been screened for technical skills, data architecture expertise, and problem-solving abilities. Pick the best for interviews.

4

Onboard and build with confidence

Boundev handles contracts and billing. Your dedicated Matching Expert stays with you throughout the collaboration, ensuring seamless data project delivery.

Find developers skilled in related data technologies

Python

Python

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SQL

SQL

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Cloud

Cloud

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About Data Engineering

01

What is a data engineer?

    A data engineer designs, builds, and maintains systems that collect, store, and process data at scale.

    They create robust data pipelines, architect data solutions, and ensure data quality for analytics and machine learning. Data engineers bridge the gap between raw data and actionable business insights.

02

Key use cases for data engineers

  • ETL/ELT pipelines – Extract, transform, and load data into warehouses
  • Real-time streaming – Build architectures using Kafka or Kinesis
  • Data lakes – Design storage on AWS S3 or Azure Data Lake
  • Data quality – Implement validation frameworks and monitoring
  • Orchestration – Create automated workflow pipelines
  • Governance – Ensure GDPR and compliance requirements
03

Data engineering tech stack

    Orchestration: Apache Airflow, Prefect, Dagster

    Processing: Apache Spark, Flink, dbt

    Warehouses: Snowflake, BigQuery, Redshift, Databricks

    Streaming: Kafka, Kinesis, Pulsar

    Storage: S3, GCS, Delta Lake

    Languages: Python, SQL, Scala

04

What does a data engineer do?

  • Design scalable data architectures
  • Build and maintain ETL/ELT pipelines
  • Implement data quality checks
  • Optimize storage and query performance
  • Ensure data security and compliance
  • Collaborate with data scientists and analysts
05

Data engineer experience levels

    Junior (0-2 years): SQL, Python basics, simple ETL tasks

    Mid-level (2-4 years): Spark, Airflow, data warehousing proficiency

    Senior (5+ years): Complex architectures, migration projects, team leadership

    Principal/Staff: Strategic platform decisions, cross-team initiatives

06

Core responsibilities

  • Design batch and real-time data pipelines
  • Build data warehouses and data lakes
  • Monitor data quality with alerting systems
  • Optimize costs and query performance
  • Document data lineage and metadata
  • Troubleshoot production issues
07

Hiring options: freelance vs in-house vs outsourced

    Freelance: Flexible for specific migration or pipeline projects

    In-house: Full-time engineers for ongoing infrastructure needs

    Outsourced (Boundev): Pre-vetted talent, verified experience, cost-effective with faster onboarding

08

Writing a data engineer job description

    Required skills:

  • SQL and Python proficiency
  • Spark, dbt experience
  • Snowflake, BigQuery, or Redshift
  • Airflow or Dagster orchestration
  • AWS, GCP, or Azure cloud platforms
  • Data modeling expertise
09

Technical interview questions

  • 1How would you design an ETL pipeline for 1TB daily data?
  • 2What's the difference between batch and stream processing?
  • 3How do you ensure data quality in production?
  • 4Explain dimensional modeling concepts
  • 5How do you handle slowly changing dimensions?
  • 6How would you debug a failed Airflow DAG?
10

Average salary for data engineers

    United States (Annual):

  • Junior: $85,000 – $110,000
  • Mid-level: $110,000 – $150,000
  • Senior/Architect: $150,000 – $200,000+
  • Specializations in real-time streaming or ML engineering can increase pay by 15-25%.

Ready to hire your perfect data engineer?

Start Hiring Today

FAQ about Hiring Data Engineers

Tell us about your plans on a brief intro call,
and we’ll start the matching process.

Start Your Data Journey Today

Share your data engineering requirements and we'll connect you with the perfect data engineer within 48 hours.

Let's work together to achieve something incredible.