Senior Data Engineer, Databricks, PySpark

Optiveum

. Design and implement scalable data products using Databricks, Delta Lake, and PySpark .

Posted 9/29/2026full-timeRemote • PolandSenior💰 €38 per hourWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and implementing scalable data products using Databricks and PySpark, with a strong focus on data pipeline development, CI/CD implementation, and data quality controls. Proficient in collaborating with cross-functional teams to gather requirements and deliver effective data solutions.

Highest-signal resume keywords

DatabricksPySparkCI/CD PipelinesData Pipeline DesignData Quality Controls

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data ModellingSpark SQLAutomated DeploymentsInfrastructure as CodeData GovernanceMetadata ManagementTroubleshootingRoot-Cause Analysis

Soft Skills

Proactive MindsetSelf-Driven

Tools & Technologies

Azure DatabricksDatabricks WorkflowsGit

Industry Keywords

Sustainability ReportingESG DataRegulatory ReportingERP DataProcurement Data

Tech Stack

Tools & technologies

AzureERPPySparkPythonSparkSQL

About the role

Key responsibilities & impact

Design and implement scalable data products using Databricks, Delta Lake, and PySpark
Build and maintain data pipelines processing complex datasets from ERP, procurement, sustainability, and external systems
Optimize workloads for performance, scalability, and cost efficiency
Build reusable engineering patterns
Implement automated data quality controls throughout the data lifecycle
Identify and resolve data issues before they impact reporting
Implement CI/CD pipelines, automated deployments, testing frameworks, and Infrastructure as Code
Support platform security controls and access management
Partner with sustainability experts, business analysts, and reporting teams on requirements gathering, solution design, and production releases

Requirements

What you’ll need

5–7 years of experience designing, developing, and operating data platforms and pipelines
Extensive hands-on experience with Databricks, including Azure Databricks and Databricks Workflows, in production environments
Expert-level proficiency in PySpark, Python, Spark SQL, Data Modelling, and Data Pipeline Design
Experience implementing CI/CD pipelines for data engineering workloads
Git-based development and version control
Solid understanding of data lineage, metadata management, governance, auditing, and validation frameworks
Fluent English, both written and spoken
Proactive, self-driven mindset with strong troubleshooting and root-cause analysis skills
Experience with sustainability reporting, ESG data, and regulatory reporting requirements (nice to have)
Knowledge of procurement, supplier, finance, and ERP data domains (nice to have)
Experience developing Databricks applications, dashboards, or user-facing data tools (nice to have)

Benefits

Comp & perks

Fully remote work from Warsaw
Full-time schedule of 40 hours/week
B2B cooperation agreement