Middle/Senior Data Engineer (#2487)

2 Months ago • 4 Years + • Data Analyst • DevOps

Job Summary

Job Description

This role involves designing, developing, and optimizing data pipelines using PySpark within an AWS ecosystem for a US-based B2B marketplace company. Responsibilities include leveraging AWS services (S3, Glue, EMR, Lambda, Redshift) to build scalable data solutions, optimizing PySpark workflows for performance and cost-efficiency, collaborating with data scientists and analysts, ensuring data quality and integrity, implementing data governance and security best practices, and documenting technical processes. The ideal candidate will have 4+ years of experience in data engineering with a focus on PySpark and AWS, proficiency in Python, a solid understanding of distributed computing, and excellent communication skills.
Must have:
  • PySpark expertise for data pipeline development
  • AWS experience (S3, Glue, EMR, Lambda, Redshift)
  • Python programming proficiency
  • Data pipeline optimization
  • Data quality and integrity assurance
Good to have:
  • Experience with Terraform or CloudFormation
  • Advanced knowledge of Apache Spark
Perks:
  • Flexible working format (remote, office, or hybrid)
  • Competitive salary and benefits package
  • Personalized career growth
  • Professional development opportunities
  • Education reimbursement
  • Corporate events and team buildings

Job Details

Role Overview:
As a Data Engineer on our Development Team, you will design, develop, and optimize data pipelines within an AWS ecosystem for a US-based B2B marketplace company. Your expertise in PySpark will be instrumental in processing large-scale datasets, ensuring the reliability and performance of our data systems. You will collaborate with cross-functional teams, including data scientists and analysts, to deliver high-impact solutions that support business objectives.

Key Responsibilities:

  • Design, develop, and implement data pipelines using PySpark within AWS environments
  • Leverage AWS services such as S3, Glue, EMR, Lambda, and Redshift for building scalable data solutions
  • Optimize PySpark workflows for performance, reliability, and cost-efficiency
  • Collaborate with stakeholders to understand data requirements and translate them into technical solutions
  • Ensure data quality and integrity through robust testing and monitoring processes
  • Implement data governance, security, and compliance best practices in all development activities
  • Document technical designs, processes, and workflows to support ongoing maintenance and team knowledge sharing.

Requirements:

  • Bachelor’s degree in Computer Science, Engineering, or a related field
  • 4 years+ of experience in data engineering, with a focus on building and optimizing data pipelines using PySpark
  • Strong experience with AWS services, including S3, Glue, Lambda, EMR, and Redshift
  • Proficiency in Python programming and familiarity with related frameworks and libraries
  • Solid understanding of distributed computing and experience with Apache Spark
  • Hands-on experience with infrastructure-as-code tools (e.g., Terraform, CloudFormation) is a plus
  • Strong analytical and problem-solving skills, with attention to detail and a proactive approach to troubleshooting
  • Excellent communication and collaboration skills, with the ability to work in a dynamic, team-oriented environment
  • Upper-Intermediate level of English
  • Ukrainian language Advanced or higher.

We offer:

  • Flexible working format - remote, office-based or flexible
  • A competitive salary and good compensation package
  • Personalized career growth
  • Professional development tools (mentorship program, tech talks and trainings, centers of excellence, and more)
  • Active tech communities with regular knowledge sharing
  • Education reimbursement
  • Memorable anniversary presents
  • Corporate events and team buildings
  • Other location-specific benefits

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