Key Responsibilities:
Design, develop, and maintain scalable data pipelines on AWS.
Build and optimize ETL/ELT workflows using PySpark, AWS Glue, and EMR.
Develop data ingestion frameworks for structured and unstructured data.
Design and implement Data Lake and Data Warehouse solutions.
Process large-scale datasets using Spark and distributed computing techniques.
Create and maintain data models supporting reporting and analytics.
Ensure data quality, governance, security, and compliance standards.
Monitor, troubleshoot, and optimize production data pipelines.
Implement CI/CD practices for data engineering solutions.
Collaborate with cross-functional teams in Agile settings.

Mandatory Technical Skills:

1) AWS Services: Amazon S3, AWS Glue, EMR, Lambda, Athena, Redshift, CloudWatch, IAM, Step Functions

2) Data Engineering: Data Lakes,



Data Warehousing, ETL / ELT Development, Batch Processing, Data Modeling, Data Migration

3) Big Data Technologies: Apache Spark, PySpark, Spark SQL

4) Programming: Python, SQL, Shell Scripting

5) DevOps & Automation: Git, CI/CD Pipelines, Infrastructure as Code (Terraform preferred)

Valuable-to-Have Skills:
Databricks
Snowflake
Kafka
Airflow
Hadoop Ecosystem
Docker
Kubernetes
Delta Lake
Streaming Data Pipelines
BFSI Domain Experience

Desired Technical Competencies:
Distributed Data Processing
Performance Tuning
Cloud Architecture
Data Security & Governance
Data Integration Patterns

📌 Aws Data Engineer 6 Years To 12 Years Mumbai
🏢 Tata Consultancy Services
📍 Mumbai