Lead Data Platform Engineer

EPAM Systems Inc; United States; Remote; USD 140,000 - 230,000; Full time

Job summary

EPAM Systems Inc. seeks a Lead Data Platform Engineer to design and run an AWS-native lakehouse platform powering governed analytics across the enterprise.

You will shape platform patterns, enable domain-owned data products with centralized governance, and guide migration from Microsoft-based platforms while improving reliability, observability, and cost.

Qualifications

• 5+ years of data engineering or data platform experience.
• Hands-on with AWS data ecosystem (S3, Glue, Lake Formation).
• Experience with lakehouse architectures or modern data platform designs.
• Knowledge of data governance, security, and access control models.
• Professional English communication.

Responsibilities

• Contribute to the enterprise data platform strategy and roadmap.
• Architect and evolve a modern AWS native lakehouse architecture.
• Define scalable, reusable data platform patterns and standards.
• Assist migration from legacy Microsoft-based data platforms.
• Apply federated data operating model and data product standards.
• Enable delivery and ongoing operation of the data platform.
• Build data ingestion pipelines and transformation frameworks.
• Ensure platform reliability, performance, and cost efficiency.
• Implement monitoring and governance controls.
• Guide tooling and technology decisions across the platform ecosystem.

Skills

Data engineering

AWS ecosystem

Lakehouse design

ETL/ELT

Data governance

Security IAM

Observability

Communication

English proficiency

Tools

S3

Glue

Lake Formation

Athena

Redshift

IAM

Job description

We are looking for a Lead Data Platform Engineer to build and run an enterprise AWS native lakehouse platform that powers governed analytics through a centralized Enterprise Data Platform (EDP). In this role, you will shape platform patterns, enable domain-owned data products with centralized governance, and guide migration from Microsoft-based platforms while improving reliability, observability, and cost.

Responsibilities

• Contribute to the enterprise data platform strategy and roadmap
• Architect and evolve a modern AWS native lakehouse architecture
• Define scalable, reusable data platform patterns and standards
• Assist migration from legacy Microsoft based data platforms
• Apply and improve the federated data operating model, including domain responsibilities and data product standards
• Enable delivery and ongoing operation of the data platform
• Build data ingestion pipelines and transformation frameworks
• Ensure platform reliability, performance, and scalability
• Implement monitoring, observability, and operational controls
• Optimize platform cost efficiency across storage, compute, and data processing workloads
• Apply and enforce data governance standards, policies, and controls
• Guide tooling and technology decisions across the platform ecosystem, including reference architectures for batch, streaming, and real-time processing

Requirements

• Proven background with 5+ years of experience in data engineering, data platforms, or related fields
• Hands-on experience with AWS data ecosystem services such as S3, Glue, and Lake Formation
• Working knowledge of Amazon DataZone, IAM, and query engines such as Athena or Redshift
• Demonstrated expertise designing and implementing lakehouse or modern data platform architectures
• Practical experience building data ingestion, ETL/ELT pipelines, and orchestration frameworks
• Strong understanding of data governance, security, and access control models
• Solid exposure to enterprise data cataloging and metadata management
• English proficiency at B2 (Upper-Intermediate) level or higher

Nice to have

• Experience migrating from Microsoft-centric data platforms such as SQL Server, SSIS, and Azure
• Familiarity with domain-oriented or data mesh operating models
• Experience supporting analytics tools such as Power BI
• Knowledge of data sharing and cross-domain data product models
• Exposure to agentic AI capabilities and data access patterns for AI workloads