Remote Data Engineer / Databricks / Work - Life Balance

Kelly Services Inc.
United States
2 months ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Experienced
Experience required
3 years minimum
Compensation
$124,800.0 - $166,400.0
Working hours
Regular working hours
Job source

Tech stack

Agile Methodology Amazon Web Services Architectural Patterns Big Data Information Engineering Data Governance SQL Databases Data Streaming Feature Engineering Azure Data Factory Sql Optimization Apache Spark
+6 more
Data Lakes Pyspark Apache Kafka Spark Streaming Machine Learning Operations Databricks

Requirements

  • 3-5 years of hands-on data engineering experience
  • Strong experience with Databricks (Delta Lake, workflows, SQL)
  • Proficiency with Apache Spark (PySpark preferred)
  • Advanced SQL skills for large-scale data processing
  • Experience with AWS or Azure data services
  • Solid understanding of data modeling and architecture patterns
  • Experience working in Agile environment

Desired Skills & Experience

  • Experience with Delta Live Tables or similar frameworks
  • Familiarity with dbt or modern transformation tools
  • Exposure to ML pipelines or feature engineering workflows
  • Experience with data governance or cataloging tools
  • Background in consulting or client-facing environments
  • Experience with streaming data (Kafka or Spark Streaming)

Benefits & conditions

Tech Breakdown

  • 70% Databricks / Spark / Cloud Data Engineering
  • 30% Data Modeling, Optimization, and Collaboration

Daily Responsibilities

  • 75% Hands-on
  • 15% Cross-Team Collaboration
  • 10% Architecture & Design

The Offer

  • Bonus eligible

You will receive the following benefits:

  • Medical, Dental, and Vision Insurance
  • Flexible Time Off (FTO)
  • Paid parental leave

About the company

A fast-growing, AI-driven digital consultancy is hiring a Data Engineer to help build modern data platforms for large enterprise clients. This role focuses on technologies like Databricks, Apache Spark (PySpark), and cloud platforms (AWS/Azure), delivering scalable solutions that power analytics and machine learning initiatives.

This opportunity stands out for its high impact and variety, so you won’t be siloed. Instead, you’ll design end-to-end pipelines, contribute to cloud lakehouse architectures, and partner closely with analytics and AI teams across multiple industries. It’s ideal for someone who wants to expand their technical depth while working on meaningful, production-level systems that directly influence business outcomes.

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