Lead Data Developer

EPAM Systems; United States; Remote; USD 120,000 - 210,000; Full time

Job summary

EPAM Systems is seeking a Lead Data Engineer to architect and strengthen the data platform behind our UCC reporting stack. You will drive data modeling across OLTP (Postgres) and OLAP (StarRocks) and guide a team through migrations and schema updates.

The role requires 5+ years in data engineering, 1+ year in leadership, and deep PostgreSQL/OLAP experience, with Docker, Kubernetes, Java/Kotlin, and ETL/ELT expertise. Strong communication and English proficiency are essential.

Qualifications

• 5+ years of hands-on professional data engineering experience.
• At least 1 year in a leadership or team management role.
• Extensive experience with PostgreSQL at scale.
• Deep knowledge of OLAP technologies (StarRocks or similar).
• Background in data warehousing principles.
• Experience with Docker for containerized services.
• Java or Kotlin development experience.
• Kubernetes for managing workloads.
• Terraform for infrastructure as code.
• Experience with Flink, Kafka, and ETL/ELT pipelines.
• Unix/Linux environments experience.
• Excellent English communication.

Responsibilities

• Troubleshoot and improve data modeling across OLTP and OLAP systems.
• Plan schema updates, backfills, and large migrations while keeping services online.
• Tune queries and storage for transactional and analytical workloads.
• Suggest architectural improvements to scale the analytics platform.
• Mentor teammates and collaborate with stakeholders.

Skills

PostgreSQL

StarRocks

Kotlin

Golang

Docker

Java

Kubernetes

Terraform

Unix/Linux

ETL/ELT

Flink

Kafka

Data modeling

AWS Aurora

Tools

AWS Aurora

Job description

We are on the lookout for an accomplished Lead Data Engineer to join our team and help architect, expand, and fortify the data platform behind our industry-leading Unified Communications and Collaboration (UCC) reporting stack.

Responsibilities

• Troubleshoot and improve data modeling approaches spanning OLTP (Postgres) and OLAP (StarRocks) database environments


• Plan and carry out schema updates, backfills, and large-scale migrations while keeping services running smoothly


• Fine-tune query performance and refine storage architecture to serve both transactional and analytical needs


• Suggest continuous enhancements and shape architectural choices that support scaling the analytics platform

Requirements

• At least 5 years of relevant hands‑on professional experience


• A minimum of one year of experience in a leadership or team management role


• Substantial, practical experience functioning as a DBA or data engineer working with relational databases such as PostgreSQL at scale


• Deep familiarity with OLAP technologies, including StarRocks or comparable columnar/analytics database systems


• Background in data warehousing principles and implementation


• Skilled at diagnosing and optimizing query performance and storage structure across both OLTP and OLAP environments


• Working knowledge of software development using Kotlin or Golang


• Strong preference for designing data solutions that are performant, efficient, and reliable at scale


• Self-driven and team-oriented, comfortable working autonomously while also guiding and mentoring colleagues


• Solid communication skills for collaborating effectively with team members and stakeholders alike


• Strong written and spoken proficiency in English at a B2 level or higher


• Nice to have Experience with AWS Aurora for handling scalable relational database management


• Knowledge of data modeling practices that support dependable and efficient data structures


• Expertise in database performance tuning to enhance query speed and storage efficiency


• Familiarity with Docker for building and managing containerized services


• Working knowledge of Java for various software development tasks


• Experience with Kubernetes for managing and scaling containerized workloads


• Familiarity with Terraform for provisioning infrastructure through code


• Comfortable operating within Unix-based environments, including Linux for pods and macOS for workstations


• Background working with data engineering pipelines, including technologies such as Flink, Kafka, and ETL/ELT workflows