Senior Data Engineer - GPT

Hiring for our leading client product based in Chennai (Hybrid) 

About Company:
➞ Join a leading company in the ad-tech space, backed by a Nasdaq-listed parent company with over 5,000+ employees globally.
➞ Specializes in providing data-driven solutions for demand-side platforms (DSP).
➞ Enables global brands to connect with target audiences through precision-driven approaches.
➞ Leverage a real-time analytics platform to optimize advertiser performance across digital platforms.

Qualifications: 

• 5-8 years of experience in data engineering, with a focus on building and managing data pipelines.
• Strong proficiency in relational databases (e.g., MySQL, PostgreSQL) and NoSQL databases (e.g., MongoDB, Cassandra).
• Experience in building data pipelines with data warehouses like Snowflake, Redshift 
• Experience in processing unstructured data stored from S3 using Athena, Glue etc.
• Hands-on experience  or knowledge with Kafka for real-time data streaming and messaging.
• Solid understanding of ETL processes, data integration, and data pipeline optimization.
• Proficiency in programming languages like Python, Java, or Scala for data processing.
• Experience with Apache Spark for big data processing and analytics is an advantage
• Familiarity with AWS cloud platforms for data infrastructure is a plus.
• Excellent problem-solving skills and attention to detail.
• Strong communication and collaboration skills, with the ability to work effectively in a team environment.

Key Responsibilities:

• Design, build, and maintain efficient and scalable data pipelines to support data integration and transformation across various sources.
• Work with relational databases (e.g., MySQL, PostgreSQL, etc.) and NoSQL databases (e.g., MongoDB, Cassandra, etc.) to manage and optimize large datasets.
• Utilize Apache Spark for distributed data processing and real-time analytics.
• Implement and manage Kafka for data streaming and real-time data integration between systems.Collaborate with cross-functional teams to gather and translate business requirements into technical solutions.
• Monitor and optimize the performance of data pipelines and architectures, ensuring high availability and reliability.
• Ensure data quality, consistency, and integrity across all systems.
• Stay up-to-date with the latest trends and best practices in data engineering and big data technologies.