Lead Data Engineer with Databricks

PROJECT INFORMATION:

Industry: Pharma

Remote work: 100%

Project language: English

Business trips: Occasionally to Warsaw and Denmark (every few months)

Project length: 12 months contracts + prolongations.

Start: ASAP

Assignment type: B2B

Summary: The Lead Data Engineer plays a critical role in leading transformative data engineering initiatives to optimize data infrastructure while ensuring high-quality data management solutions across the organization.

Main Responsibilities:

• Work collaboratively with teams of data engineers to foster a culture of innovation.
• Execute data engineering strategies in collaboration with stakeholders.
• Define and implement best practices and standards for data engineering.
• Facilitate efficient data management by enabling data pipelines and tools.
• Ensure delivery of scalable and efficient data management solutions.
• Collaborate with cross-functional teams to address technical requirements and improve efficiency.
• Provide capabilities for achieving data quality and system performance goals.
• Promote a culture of professional growth, diversity, and inclusion in a high-performance team.

We offer:

• Long-term cooperation.
• Transparently built relations based on trust and fair play.
• Co-financed benefits: Medicover card, Multisport card.

Key Requirements:

• A master’s degree in Data, Computer Science, or a related field.
• 8+ years experience in software and data engineering, including at least 4 years in Data Pipeline Engineering.
• Strong proficiency in Databricks.
• Extensive experience with Python for data manipulation and automation.
• Proficient in SQL for complex queries and data extraction.
• Hands-on with DevOps and CI/CD processes.
• Expertise in data modeling and integration.
• 4+ years experience with Cloud Technologies like Azure and AWS.
• Familiarity with life sciences regulatory requirements.
• Proven leadership and exceptional communication skills.

Nice to Have:

• Certifications in Cloud Technologies.
• Experience designing scalable data engineering solutions.