Data Platform Lead - Spark, Python & Cloud Pipelines

EPAM Systems; Turkey; On-site; TRY 5,914,000 - 8,871,000; Full time

Benefits offered by this job

Extra leave days

Referral bonuses

Private health insurance

Well-being events

Job summary

EPAM Systems, Inc. is seeking a hands-on Lead Data Software Engineer with deep Apache Spark expertise to drive end-to-end data pipelines and infrastructure design. You will mentor engineers, collaborate with cross-functional teams, and align technical work with the company’s strategic data roadmap.

The role emphasizes scalable ingestion, performance tuning, and robust SDLC practices to deliver high-quality data solutions across cloud environments. Strong communication with customers is required.

Qualifications

• 5+ years of data software engineering experience in large-scale distributed systems.
• Strong design and architecture skills for data ingestion and processing pipelines.
• Experience leading teams and communicating with customers about technical roadmaps.

Responsibilities

• Design, implement, and architect robust data ingestion pipelines at scale.
• Lead a cross-functional team and mentor engineers to deliver high-quality, scalable solutions on schedule.
• Manage full tech stack including configuration, monitoring, and performance tuning of data solutions.
• Collaborate with data architects to translate business requirements into technical specs.
• Lead code reviews and SDLC best practices with a focus on quality and reliability.
• Create and maintain comprehensive technical documentation.

Skills

Python

SQL

Apache Spark

Kafka

Airflow

Databricks

Snowflake

Cloud proficiency

Team leadership

Tools

Databricks

Snowflake

Job description

EPAM Systems, Inc. is seeking a hands-on Lead Data Software Engineer with deep Apache Spark expertise to drive end-to-end data pipelines and infrastructure design. You will mentor engineers, collaborate with cross-functional teams, and align technical work with the company’s strategic data roadmap.

The role emphasizes scalable ingestion, performance tuning, and robust SDLC practices to deliver high-quality data solutions across cloud environments. Strong communication with customers is required.