Data Engineering - Technical Lead

Paytm; Bengaluru; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

Paytm is seeking an experienced Data Engineer in Bengaluru, India, to design and maintain scalable data pipelines. You will collaborate with data scientists to implement data integration solutions and ensure data quality, contributing to performance optimization.

The ideal candidate has over six years of experience in data engineering, strong programming skills in Java, Scala, and SQL, and familiarity with tools like Apache Spark and Kafka. Paytm offers a dynamic work environment focused on innovation in financial services.

Qualifications

• 6+ years of progressive experience in data architecture and engineering.
• Solid hands-on with 4+ years of experience in scalable microservices.
• Demonstrated ability to lead and collaborate cross-functionally.

Responsibilities

• Design and maintain scalable data pipelines for data processing.
• Implement data integration solutions from multiple sources.
• Optimize data pipelines for performance and reliability.

Skills

Java

Scala

SQL

Apache Spark

Kafka

Spring Boot

Kubernetes

Docker

Airflow

Education

Bachelor's degree in computer science or related field

Tools

Apache Cassandra

MongoDB

Oracle

PostgreSQL

Job description

Paytm is India's leading mobile payments and financial services distribution company. Pioneer of the mobile QR payments revolution in India, Paytm builds technologies that help small businesses with payments and commerce. Paytm’s mission is to serve half a billion Indians and bring them to the mainstream economy with the help of technology.

Job Summary

Build systems for collection & transformation of complex data sets for use in production systems. Collaborate with engineers on building & maintaining back-end services. Implement data schema and data management improvements for scale and performance. Provide insights into key performance indicators for the product and customer usage. Serve as team's authority on data infrastructure, privacy controls and data security. Collaborate with appropriate stakeholders to understand user requirements. Support efforts for continuous improvement, metrics and test automation. Maintain operations of live service as issues arise on a rotational, on-call basis. Verify whether data architecture meets security and compliance requirements and expectations. Should be able to fast learn and quickly adapt at rapid pace. java/scala, SQL.

Minimum Qualifications

• Bachelor's degree in computer science, computer engineering or a related field, or equivalent.
• 6+ years of progressive experience demonstrating strong architecture, programming and engineering skills.
• Firm grasp of data structures, algorithms with fluency in programming languages like Java, Python, Scala. Strong SQL language and should be able to write complex queries.
• Strong Airflow like orchestration tools. Demonstrated ability to lead, partner, and collaborate cross functionally across many engineering organizations.
• Experience with streaming technologies such as Apache Spark, Kafka, Flink. Backend experience including Apache Cassandra, MongoDB and relational databases such as Oracle, PostgreSQL, AWS/GCP.
• Solid hands on with 4+ years of experience. Strong communication and soft skills. Knowledge and/or experience with containerized environments, Kubernetes, docker.
• Experience in implementing and maintaining highly scalable micro services in Rest, Spring Boot, GRPC. Appetite for trying new things and building rapid POCs.

Key Responsibilities

• Design, develop, and maintain scalable data pipelines to support data ingestion, processing, and storage.
• Implement data integration solutions to consolidate data from multiple sources into a centralized data warehouse or data lake.
• Collaborate with data scientists and analysts to understand data requirements and translate them into technical specifications.
• Ensure data quality and integrity by implementing robust data validation and cleansing processes.
• Optimize data pipelines for performance, scalability, and reliability.
• Develop and maintain ETL (Extract, Transform, Load) processes using tools such as Apache Spark, Apache NiFi, or similar technologies.
• Monitor and troubleshoot data pipeline issues, ensuring timely resolution and minimal downtime.
• Implement best practices for data management, security, and compliance.
• Document data engineering processes, workflows, and technical specifications.
• Stay up-to-date with industry trends and emerging technologies in data engineering and big data.