Azure Data Engineering

Azure Data Engineering

DataZymes Analytics is a cutting-edge and innovative data analytics company working with Life Sciences clients that empowers businesses and organizations to unlock the true potential of their data. Our mission is to harness the power of data and transform it into actionable insights, enabling our clients to make informed decisions and gain a competitive edge in today's data-driven world.

With a team of highly skilled data scientists, analysts, and engineers, DataZymes Analytics is committed to delivering top-notch solutions tailored to the unique needs of each client. Aided by our suite of cutting-edge products and accelerators, we are helping our Pharma clients on their Data to Insights journey. In a short span, we have built a high-performance team in focused practice areas, built digital-enabled solutions, and are working with some marquee names in the US healthcare industry.

What sets us apart is our dedication to accuracy, efficiency, and innovation. We utilize the latest technologies and industry best practices to ensure the highest quality of work and the fastest turnaround times. Moreover, our agile approach allows us to adapt quickly to changing requirements and deliver exceptional results within the most demanding timelines.

• Experience in setting up data pipelines in Azure.
• Experience with Azure Data Factory
• Experience with setting up Azure Data Lake with structured/unstructured data.
• Candidate needs to be a self-starter and should be able to design, develop and deliver solution without much assistance or supervision.
• Strong knowledge of Azure services - Storage (AFS, Blob, Data Lake), Azure Functions.
• Strong knowledge of database - Should be able to write complex queries, schema design.
• Ability to set up CI/CD pipelines in Azure through GitHub.

Qualifications

• Bachelor’s or master’s degree in computer science, Information Technology
• Experience with batch job scheduling and identifying data/job dependencies.

• Preferred Skills:

• Proficiency in data warehousing, ETL, and big data processing.
• Familiarity with the Pharma domain is a plus.