Design and implement scalable data pipelines using AWS services such as
S3, Glue, PySpark, and EMR
.
Develop and maintain robust data transformation scripts using
Python and SQL
.
Optimize data storage and retrieval using
Redshift, RDS, DynamoDB
, and other AWS database services.
Build and manage data warehouse layers tailored to business requirements.
Apply ETL and data modeling best practices to ensure efficient data flow and structure.
Ensure high data quality, availability, and consistency for analytics and reporting.
Collaborate with data analysts, BI teams, and business stakeholders to understand requirements and deliver solutions.

Required Qualifications

Bachelors/Master’s degree in Computer Science, IT, Data Engineering, or related field.
Strong hands-on experience with AWS data services such as
S3, Glue Studio, Glue ETL, Redshift, Athena, EMR
.
Excellent proficiency in
SQL and Python
for data processing.
Experience in
batch job scheduling
and managing data dependencies.

Preferred Skills

Experience working with
data warehousing
, ETL frameworks, and big data processing.
Familiarity with
data lake architecture
and schema evolution.
Experience in Life Sciences / Pharmaceutical analytics is a plus.

Important Note

not DevOps-focused

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