Middle/Senior Data Engineer (#2487)
About the job
Summary
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
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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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