Data Engineer

Job Title: Senior 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 MongoDB, Kafka, and other streaming or NoSQL 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 MongoDB , 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 DevOps 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 NoSQL databases like MongoDB and real-time streaming platforms like Kafka.