Job Description

Requirements:

• 5+ years of hands-on experience with Databricks development, supported by relevant certifications (proof required).
• Proven expertise in building, optimizing, and troubleshooting production-grade ETL pipelines.
• Strong experience designing and implementing medallion architecture models (raw, curated, and trusted layers) within Databricks environments.
• Solid understanding of dimensional data modeling to support business intelligence, enterprise reporting, and single source of truth initiatives.
• Advanced expertise in orchestrating data ingestion, transformation, and integration workflows across multiple systems and data formats.
• Ability to interpret operational requirements, convert them into technical solutions, and clearly communicate the business impact of engineering decisions.
• Demonstrated ability to take ownership, work independently, and drive projects successfully in dynamic or ambiguous environments.
• Experience with Azure Data Lake, Azure Data Factory, and related Azure services is considered a strong plus.
• Strong communication and collaboration skills, with the confidence to provide constructive feedback, challenge assumptions, and advocate for best practices.
• Ability to adapt quickly, stay results-oriented, collaborate effectively, maintain a positive attitude, and lead with empathy.
• Actively contribute to a culture of collaboration, continuous improvement, knowledge sharing, and openness to innovation.
• Demonstrated ability to provide meaningful insights and respectfully push back when needed, always focusing on achieving the best possible outcomes for the team and project.

Responsibilities:

• Design, develop, and maintain complex ETL pipelines in Databricks, ensuring scalable and high-performance data integration across multiple source systems.
• Implement and optimize medallion architecture within Databricks by establishing structured data zones (raw, curated, and trusted) to support governed, enterprise-level reporting.
• Develop and enhance dimensional data models that provide analytics-ready business views and support automated dashboards and KPI reporting frameworks.
• Collaborate closely with cross-functional teams, including data stewards, IT teams, and business stakeholders, to translate operational requirements into effective technical solutions while proactively identifying dependencies and driving alignment.
• Contribute to architectural and technical decisions by recommending best practices, challenging assumptions where necessary, and ensuring the scalability, durability, and flexibility of the data platform.
• Proactively identify and resolve integration issues, data quality concerns, and process bottlenecks, while providing actionable insights and constructively highlighting potential project risks or inefficiencies.
• Support documentation and knowledge-sharing initiatives to enable teams and clients to independently maintain and enhance data solutions over time.