Senior Data Engineer (AWS)

JOB TITLE: Senior Data Engineer (AWS)
WORKPLACE TYPE: Hybrid
JOB LOCATION: Bengaluru, Karnataka, India - Only Locals Preferred
WORK MODE: Hybrid - 4 to 5 Days WFO | WFH Flexibility up to 6 days/month
JOB TYPE: Full-time
EXPERIENCE: 5 to 8 Years
INDUSTRY: Pharmaceutical / Life Sciences / Healthcare Analytics
AVAILABILITY: Immediate Joiner / Serving Notice Period (Joining within 1 week)
CTC STRUCTURE: CTC inclusive of 20% variable
JOB DESCRIPTION:
About the Role
We are hiring a Senior Data Engineer (AWS) for a next-generation analytics and data science company founded in 2016, working with marquee names in the US healthcare industry. The role is based in Bengaluru and requires strong hands-on expertise in AWS data warehousing, ETL/ELT pipelines, and data modelling.
This is a hands-on engineering role building and maintaining scalable cloud data platforms that power advanced reporting and analytics for customer acquisition and operational efficiency.
Roles & Responsibilities
1. Create and maintain optimal data pipeline architecture for ETL/ELT into structured data
2. Assemble large, complex data sets that meet business requirements and create multi-dimensional models like Star Schema and Snowflake Schema
3. Build and manage scalable data warehouses including Fact tables, Dimensional tables and ingest datasets into cloud-based tools
4. Identify, design, and implement internal process improvements including automating manual processes and optimizing data delivery
5. Collaborate with stakeholders and Global IT to ensure seamless integration of data with internal data marts, enhancing advanced reporting
6. Setup and maintain data ingestion, streaming, scheduling, and job monitoring automation using AWS services - Lambda, Glue, S3, Redshift, Code Pipeline
7. Build analytics tools that utilize the data pipeline to provide actionable insights
8. Work with analytics and data science teams to create data tools and support data infrastructure needs
9. Utilize GitHub for version control, code collaboration, code reviews, branching strategies and CI/CD
10. Ensure data quality, consistency, privacy and compliance with regulations like GDPR
Mandatory Requirements
Experience:
· 5-8 years total, with at least 4+ years hands-on Data Engineering experience
· Recent 2+ years must be in AWS cloud data warehouses and AWS cloud services
· Current/recent role must clearly show hands-on AWS data platforms and ETL pipeline implementation with specific projects and tools mentioned in resume
Technical Skills:
· Advanced SQL + Relational Databases + Cloud Data Warehouse (AWS Redshift mandatory)
· Expert in Data Warehouse Design: Fact tables, Dimension tables, Star Schema, Snowflake Schema, Normalization/De-normalization, OLAP cubes, Schema evolution
· ETL/ELT Architecture: Hands-on in creating and maintaining optimal ETL/ELT pipelines into structured data
· AWS Data Stack: Hands-on with AWS Lambda, Glue, S3, Redshift, Code Pipeline (CI/CD), EC2, EMR
· Big Data: Experience building & optimizing big-data pipelines, PARQUET compression, SQL performance tuning, PySpark
· Version Control: GitHub - version control, collaboration, code reviews, branching strategies, continuous integration
· Other: Message Queuing, Stream Processing, highly scalable big-data stores, Data Quality, Root Cause Analysis on structured/unstructured datasets
· Compliance: Data privacy & GDPR compliance experience
· Methodology: Familiarity with Agile working models
Education:
Bachelor's or Master's degree in Technology / Computer Science background
Preferred:
Healthcare / Pharmaceutical / Life Sciences domain experience
NoSQL, Snowflake Schema, OLAP experience is a plus
Additional Information
Location: Bengaluru - Only Locals
Work Mode: Hybrid - 4-5 days in office
Availability: Must be able to start within 1 week
CTC is inclusive of 20% variable
Interview Process
1. Tech Round 1
2. Tech Round 2
3. Final Discussion
SKILLS REQUIRED:
SQL, AWS Redshift, ETL, ELT, Data Modelling, AWS Lambda, AWS Glue, AWS S3, AWS EC2, AWS EMR, AWS CodePipeline, CI/CD, GitHub, Data Warehouse Design, Star Schema, Snowflake Schema, Dimensional Modeling, Fact Tables, Data Pipeline Architecture, PySpark, Big Data, PARQUET, Message Queuing, Stream Processing, Data Quality, GDPR, OLAP, Relational Databases

Work arrangement
No