AWS Data Engineer

Key Responsibilities  As a Senior Data Engineer, you will:
Data Pipeline Development:  Design, build, and maintain scalable data pipelines using PySpark and Python.
AWS Cloud Integration:  Work with AWS cloud services (S3, Lambda, Glue, EMR, Redshift) for data ingestion, processing, and storage.
ETL Workflow Management:  Implement and maintain ETL workflows using DBT and orchestration tools (e.g., Airflow).
Data Warehousing:  Design and manage data models in Snowflake, ensuring performance and reliability.
SQL Optimization:  Utilize SQL for querying and optimizing datasets across different databases.
Data Integration:  Integrate and manage data from Mongo

DB, Kafka, and other streaming or No

SQL sources.
Collaboration & Support:  Collaborate with data scientists, analysts, and other engineers to support advanced analytics and Machine Learning (ML) initiatives.
Data Quality & Governance:  Ensure data quality, lineage, and governance through best practices and tools.
Mandatory Skills & Experience   Strong programming skills in  Python  and  PySpark .
Hands-on experience with  AWS data services  (S3, Lambda, Glue, EMR, Redshift).
Proficiency in  SQL  and experience with  DBT  for data transformation.

Experience with  Snowflake  for data warehousing.
Knowledge of  Mongo

DB ,  Kafka , and data streaming concepts.
Good understanding of  data architecture, data modeling, and data governance .
Familiarity with large-scale data platforms.
Essential Professional Skills   Excellent  problem-solving skills .
Ability to work  independently or as part of a team .

Experience with  CI/CD  and  Dev Ops practices  in a data engineering environment (Plus).
Qualifications   Proven hands-on experience working with  large-scale data platforms .
Strong background in  Python, PySpark, AWS , and modern data warehousing tools such as  Snowflake and DBT .
Familiarity with  No

SQL databases  like Mongo

DB and real-time streaming platforms like Kafka.