Lead Software Engineer - Data

EPAM Systems; Bengaluru; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

EPAM Systems in Bengaluru seeks a Lead Software Engineer - Data to architect and deliver enterprise-scale data solutions. You will lead a data engineering team, design distributed pipelines with Spark, and implement streaming and batch data processing leveraging Azure/AWS Databricks.

The ideal candidate has 8-14 years in Big Data, strong SQL, NoSQL experience, and proven leadership within Agile environments. This role emphasizes end-to-end project delivery and cloud-based data services.

Qualifications

• 8-14 years of hands-on Big Data experience.
• Strong Spark and distributed computing knowledge.
• Experience with Hadoop, Sqoop, Hive/Impala.
• Experience with messaging systems like Kafka/RabbitMQ.
• Working knowledge of Hive and Impala for querying.
• SQL expertise with joins, stored procedures, and relational design.
• NoSQL experience with HBase, Cassandra, or MongoDB.
• Experience with ETL methodologies and Databricks on cloud.
• Proven leadership and team coaching ability.
• Experience applying Agile methodologies.

Responsibilities

• Direct the design and rollout of enterprise Big Data solutions across the organization.
• Build and fine-tune distributed data pipelines using Apache Spark.
• Produce efficient Python code to support data engineering efforts.
• Develop stream-processing systems with Apache Storm or Spark-Streaming.
• Consolidate data from RDBMS, ERP systems, and file inputs.
• Utilize ETL frameworks to ensure dependable data transformation and delivery.
• Optimize Spark performance to meet business and tech needs.
• Lead adoption of cloud data services on Azure/AWS via Databricks.
• Oversee and coach a team of engineers to deliver high-quality data solutions.
• Champion Agile practices across the SDLC.

Job description

We're seeking a Lead Software Engineer - Data who will architect, develop, and fine-tune enterprise-scale data solutions and lead a team through the entire Big Data implementation process. The ideal candidate brings strong technical knowledge of distributed computing and contemporary data processing tools, combined with proven leadership skills to manage projects using Agile methodologies.

Responsibilities

• Direct the design and rollout of Big Data solutions organization-wide
• Build and fine-tune distributed data pipelines utilizing Apache Spark
• Produce efficient and scalable Python code to support data engineering efforts
• Develop stream-processing systems using technologies like Apache Storm or Spark-Streaming
• Consolidate data originating from various sources, such as RDBMS, ERP systems, and file-based inputs
• Utilize ETL methodologies and frameworks to guarantee dependable data transformation and delivery
• Optimize Spark job performance to satisfy both business and technical needs
• Lead the team's adoption of native cloud data services on Azure or AWS using Databricks
• Oversee and coach a team of engineers to deliver top-quality data solutions effectively
• Champion Agile practices across the entire software development lifecycle

Requirements

• 8-14 years of hands-on experience with Big Data and associated technologies
• Strong command of distributed computing concepts and Apache Spark
• Skilled in Hadoop v2, MapReduce, HDFS, and Sqoop
• Hands-on experience with messaging platforms like Kafka or RabbitMQ
• Working knowledge of Big Data querying tools including Hive and Impala
• Solid grasp of SQL queries, joins, stored procedures, and relational schema design
• Practical experience with NoSQL databases like HBase, Cassandra, or MongoDB
• Familiarity with ETL methodologies and frameworks
• Hands-on background with native cloud data services on Azure or AWS via Databricks
• Proven ability to lead teams effectively
• Track record of designing and deploying Big Data solutions
• Hands-on experience applying Agile methodology