Senior Data Engineer: Azure Databricks, PySpark & Global Scale

EPAM Systems

Turkey

On-site

TRY 4,442,000 - 6,417,000

Full time

25 hours ago
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Benefits offered by this job

Upskilling program
Mentoring programs
Certification support
English courses
Private health insurance
Well-being events

Job summary

EPAM Systems is seeking a Senior Data Engineer / Databricks Engineer to join a project focused on collecting, processing, and analyzing vehicle telemetry data for a major European automotive manufacturer. The platform operates at scale in Azure Databricks, running about 200 Databricks jobs across Europe, North America, and China, with a new China deployment planned.

The role emphasizes building scalable, cost-aware infrastructure, improving observability, and optimizing PySpark workloads for

Qualifications

  • 3+ years hands-on data engineering experience.
  • Experience with Azure Databricks and PySpark.
  • Strong Python development skills.
  • Experience optimizing large-scale Spark workloads.
  • Background in production data platforms and monitoring.

Responsibilities

  • Monitor health, stability, and performance of production data platform.
  • Investigate and resolve performance issues and incidents.
  • Analyze Databricks workloads and optimize PySpark jobs for performance and cost.
  • Support 200+ existing Databricks jobs and pipelines.
  • Design scaling strategies with cost-focused infrastructure.

Skills

Azure Databricks
PySpark
Python
Performance optimization
Monitoring & observability
Cloud cost optimization
English (B2)

Tools

GitHub Actions

Job description

EPAM Systems is seeking a Senior Data Engineer / Databricks Engineer to join a project focused on collecting, processing, and analyzing vehicle telemetry data for a major European automotive manufacturer. The platform operates at scale in Azure Databricks, running about 200 Databricks jobs across Europe, North America, and China, with a new China deployment planned.

The role emphasizes building scalable, cost-aware infrastructure, improving observability, and optimizing PySpark workloads for

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