Responsibilities
- Build data pipelines, Big data processing solutions and data lake infrastructure using various Big data and ETL technologies
- Assemble and process large, complex data sets that meet functional non-functional business requirements ETL from a wide variety of sources like MongoDB, S3, Server-to-Server, Kafka etc., and processing using SQL and big data technologies
- Build analytical tools to provide actionable insights into customer acquisition, operational efficiency and other key business performance metrics Build interactive and ad-hoc query self-serve tools for analytics use cases
- Build data models and data schema for performance, scalability and functional requirement perspective Build processes supporting data transformation, metadata, dependency and workflow management
- Research, experiment and prototype new tools/technologies and make them successful
Skill Requirements
- Must have-Strong in Python/Scala
- Must be highly proficient in AI-assisted coding. Fluency with AI tools such as Cline, Cursor, or similar is expected as part of a modern engineering workflow. They should be able to use these tools effectively, write good prompts, and guide the AI in a smart and responsible manner to produce high-quality, maintainable code.
- Must have experience in Big data technologies like Spark, Hadoop, Athena / Presto, Redshift, Kafka etc
- Experience in various file formats like parquet, JSON, Avro, orc etc
- Experience in workflow management tools like airflow Experience with batch processing, streaming and message queues
- Any of visualization tools like Redash, Tableau, Kibana etc
- Experience in working with structured and unstructured data sets
- Strong problem solving skills
Good to have
- Exposure to NoSQL like MongoDB
- Exposure to Cloud platforms like AWS, GCP, etc
- Exposure to Microservices architecture
- Exposure to Machine learning techniques
Findem Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Findem and has not been reviewed or approved by Findem.
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Healthcare Strength — Company materials describe comprehensive medical, dental, and vision coverage for employees and dependents, with most costs covered by the employer. This signals strong core health benefits designed to support employees and their families.
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Equity Value & Accessibility — Employees are granted company equity, with postings highlighting equity grants as part of standard packages. This ensures team members share in the company’s achievements and potential upside.
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Leave & Time Off Breadth — Policies include flexible PTO that encourages rest and recharge, alongside dedicated parental leave supporting families. References to wellness days and paid holidays further reinforce breadth of time-off options.
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What We Do
Findem is the AI platform built for talent decisions. Its Labeling Engine transforms billions of unstructured people data points into verified Success Signals — context about what drives success — and Relationship Signals — insight into how people are connected and where influence flows. Together, these give customers a competitive edge in hiring, executive search, mobility, learning, development and workforce planning. That’s why leaders like Nutanix and RingCentral rely on Findem to improve pipeline quality, reduce costs, and deliver faster, more consistent talent impact. Named one of America’s Most Innovative Companies, Findem is redefining how organizations turn people data into business advantage. Learn more at www.findem.ai.
Why Work With Us
At Findem, we’re building a fast-paced culture of experimentation. Our global team of product innovators, talent experts, and former practitioners collaborates to solve some of talent acquisition’s most persistent challenges. We’re motivated by the potential to transform talent and solve our customers’ problems in new ways.
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