Walk in drive - AWS Data Engineering - from 11AM - 4PM

Reposted 3 Days Ago
Be an Early Applicant
Bangalore, Bengaluru, Karnataka
Mid level
Information Technology • Software • Analytics • Biotech
The Role
Design and implement AWS data pipelines, develop data processing scripts in Python and SQL, and optimize data storage solutions.
Summary Generated by Built In

Company Description

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.

Job Description

  • Design and implement data pipelines using AWS services such as S3, Glue, PySpark and EMR.
  • Develop and maintain data processing and transformation scripts using Python and SQL.
  • Optimize data storage and retrieval using AWS database services such as RDS, Redshift and DynamoDB.
  • Build different types of data warehousing layers based on specific use cases.
  • Utilize expertise in SQL and have a strong understanding of ETL and data modeling.
  • Ensure the accuracy and availability of data to customers and understand how technical decisions can impact their business’s analytics and reporting.

Qualifications

  • 3-5 Years of experience
  • Preference for immediate joiners and candiates who can join us within 30 days 
  • Bachelor’s or Master’s Degree in Computer Science, Computer Engineering, or Information Technology
  • Experience with AWS cloud and AWS services such as S3 Buckets, Glue Studio, Redshift, Athena, Lambda, and SQS queues.
  • 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.

Top Skills

Aws Dynamodb
Aws Emr
Aws Glue
Aws Rds
Aws Redshift
Aws S3
Python
SQL
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The Company
HQ: Philadelphia, PA
104 Employees
On-site Workplace
Year Founded: 2016

What We Do

Catalyzing Innovation in Pharma with Data-driven Digital Products.

The idea of DataZymes germinated with the realization that Pharma Commercial teams had few alternatives to the antiquated and inefficient solutions offered by traditional consulting and technology companies. Already a laggard in analytical maturity, the Pharma industry had been facing challenges to adapt to a Big Data world.

We saw that the products offered by technology companies were too rigid and generic to handle novel problems. The custom solutions offered by consulting organizations took too long to deploy and required many services to maintain and improve.

We felt the need for a different approach to finding solutions and we knew it would take a different kind of company to build it. That’s why DataZymes.

We’re focused on creating the world’s best user experience for working with data, one that empowers people to ask and answer complex questions without requiring them to master querying languages, statistical modeling, or the command line. To achieve this, we are building platforms for integrating, managing, and securing data on top of which we layer applications for fully interactive machine-driven, human-assisted analysis.

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