Full-Time Engineering Sr Analyst

We are looking for an experienced Senior Data Engineer with a strong foundation in Python, SQL, and Azure, and hands-on expertise in Databricks.

In this role, you will build and maintain scalable data pipelines and architecture to support analytics, data science, and business intelligence initiatives.

You’ll work closely with cross-functional teams to drive data reliability, quality, and performance.

Responsibilities:

• Migrate legacy data infrastructure while ensuring performance, security, and scalability.
• Build and optimize data systems, pipelines, and analytical tools, leveraging a bronze-silver-gold architecture.
• Develop and configure Databricks notebooks and pipelines for ingestion of data from both on-premise and cloud environments, following native Databricks standards.
• Collaborate with stakeholders to translate business needs into robust data solutions, and effectively communicate technical progress, risks, and recommendations.
• Implement strong data governance and access control mechanisms to ensure data quality, security, and compliance.
• Conduct advanced data analysis to support decision-making and reporting needs.
• Apply QA testing practices within data workflows, particularly in healthcare environments.
• Drive initiatives in Data Discovery, Data Lineage, and Data Quality across the organization.

Requirements:

• Studies in computer science, Engineering, or a related field.
• 3+ years of hands-on experience in data engineering, with at least 2 years working with Azure or Databricks
• Experience working with Azure Cloud, CI/CD pipelines, and Agile methodologies
• Proficiency in developing and managing Databricks notebooks and implementing data engineering frameworks.
• Strong programming skills in Python for data processing and automation.
• Advanced proficiency in SQL for querying and transforming large datasets.
• Solid understanding of data modelling, warehousing, and performance optimization techniques.
• Proven experience in data cataloging and inventorying large-scale datasets.
• Pyspark experience would be a plus

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