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Senior Data Engineer

Role overview

Qualifications

  • 5+ years of experience as a Data Engineer, building scalable data pipelines
  • Expertise in SQL and hands-on experience building performant datasets in BigQuery
  • Proficiency in Python and PySpark for scalable data processing
  • Understanding of data modeling, ELT/ETL patterns, and data quality best practices

Responsibilities

  • Design and deliver batch and real-time ETL/ELT pipelines across cloud environments
  • Write and optimize advanced SQL transformations and build performant BigQuery data models
  • Implement scalable data processing solutions using Python and PySpark
  • Troubleshoot complex pipeline issues and continuously improve performance

Key facts

Hard skills

Other skills

  • Collaboration
  • Troubleshooting (Problem Solving)

About the company

EPAM Systems logo

EPAM Systems

IT Services & IT Consulting

Since 1993, EPAM Systems, Inc. (NYSE: EPAM) has used its software engineering expertise to become a leading global provider of digital engineering, cloud and AI-enabled transformation services, and a leading business and experience consulting partner for global enterprises and ambitious startups. We address our clients’ transformation challenges by fusing EPAM Continuum’s integrated strategy, experience and technology consulting with our 30+ years of engineering execution to speed our clients’ time to market and drive greater value from their innovations and digital investments. We make GenAI real with our AI LLM orchestration, testing and engineering solutions, EPAM DIAL, EPAM EliteA™ and EPAM AI/RUN™, respectively. We deliver globally, but engage locally with our expert teams of consultants, architects, designers and engineers, making the future real for our clients, our partners and our people around the world. We believe the right solutions are the ones that improve people’s lives and fuel competitive advantage for our clients across diverse industries. Our thinking comes to life in the experiences, products and platforms we design and bring to market. Added to the S&P 500 and the Forbes Global 2000 in 2021 and recognized by Glassdoor and Newsweek as a Top 100 Best Workplace, our multidisciplinary teams serve customers across six continents. We are proud to be among the top 15 companies in Information Technology Services in the Fortune 1000 and to be recognized as a leader in the IDC MarketScapes for Worldwide Experience Build Services, Worldwide Experience Design Services and Worldwide Software Engineering Services. Learn more at www.epam.com.

Company details

Company typeXLarge
IndustryIT Services & IT Consulting
Company size10001

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Job description

We are looking for a Senior Data Engineer to design and deliver scalable data pipelines and high-performance analytical solutions using SQL/BigQuery, Spark/PySpark, and Python on Google Cloud. This role focuses on building reliable, cloud-native data products that enable advanced reporting, analytics, and decision-making across the organization. Responsibilities Design and deliver batch and real-time ETL/ELT pipelines across cloud environments to support analytics and reporting Write and optimize advanced SQL transformations and build performant, cost-efficient BigQuery data models Implement scalable data processing solutions using Python and PySpark, ensuring maintainable and high-quality code Build robust data models and apply validation practices to maintain accuracy and reliability Use GCP services such as BigQuery, Dataflow, Cloud Composer, Pub/Sub, and GCS to build and operate modern data platforms Troubleshoot complex pipeline issues and continuously improve compute, storage, and processing performance Collaborate with engineering, analytics, and business teams while contributing to CI/CD, code reviews, and testing standards Requirements 5+ years of experience as a Data Engineer, building scalable data pipelines and working with cloud-based data ecosystems Expertise in SQL and hands-on experience building performant datasets in BigQuery or similar cloud data warehouses Proficiency in Python and PySpark for scalable data processing in distributed environments Understanding of data modeling, ELT/ETL patterns, and data quality best practices Familiarity with Google Cloud Platform, particularly BigQuery, Dataflow, and Cloud Composer, GCS, or equivalent cloud data services Background in building scalable data pipelines, both batch and near real-time, in a cloud-native environment Proficiency with version control, CI/CD pipelines, and automated testing frameworks Capability to troubleshoot and optimize performance across compute, storage, and processing layers Nice to have Experience with Infrastructure-as-Code, including Terraform, Ansible, and Chef Knowledge of shell scripting Experience in financial services or regulated environments

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Marcus Rivera

Chief Revenue Officer

m.rivera@company.com
linkedin.com/in/marcusrivera
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