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.
- 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.
Originally posted on Himalayas
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