Our Client :is a leading revenue intelligence platform, combining automation and human research to deliver 95% data accuracy across their published contact data. With a growing database of 5 million+ human-verified contacts and over 70 million machine-processed contacts, they offer one of the largest collections of direct dial contacts in the industry. Their dedicated research team re-verifies contacts every 90 days, ensuring exceptional data accuracy and quality.
Location: Remote (Pan India)
Shift Timings: 2:00 PM – 11:00 PM IST
Reporting To: CEO or assigned Lead by Management.
Responsibility :
* Design and build scalable data pipelines for extraction, transformation, and loading (ETL) using the latest Big Data technologies.
* Identify and implement internal process improvements like automating manual tasks and optimizing data flows for better performance and scalability.
* Partner with Product, Data, and Engineering teams to address data-related technical issues and infrastructure needs.
* Collaborate with machine learning and analytics experts to support advanced data use cases.
Key Requirements :
* Bachelor’s degree in Engineering, Computer Science, or a relevant technical field.
* 10+ years of recent experience in Data Engineering roles.
* Minimum 5 years of hands-on experience with Apache Spark, with strong understanding of Spark internals.
* Deep knowledge of Big Data concepts and distributed systems.
* Proficiency in coding with Scala, Python, or Java, with flexibility to switch languages when required.
* Expertise in SQL, and hands-on experience with PostgreSQL, MySQL, or similar relational databases.
* Strong cloud experience with Databricks, including Delta Lake.
* Experience working with data formats like Delta Tables, Parquet, CSV, JSON.
* Comfortable working in Linux environments and scripting.
* Comfortable working in an Agile environment.
* Machine Learning knowledge is a plus.
* Must be capable of working independently and delivering stable, efficient and reliable software.
* Experience supporting and working with cross-functional teams in a dynamic environment.
#J-18808-Ljbffr