Data Engineer With Scala And Spark Pyspark

Capco; Gurugram District; Hybrid; INR 1,500,000 - 2,100,000; Full time

Job summary

Capco is hiring a Data Engineer to design and build large-scale data processing solutions. You will work with Scala, Spark, and Java to develop high-volume data pipelines capable of handling tens of millions of records daily.

The role involves API/microservice integration, collaboration with cross-functional teams, and performance optimization in a hybrid Capco office environment. The ideal candidate has 4+ years of experience in data engineering, strong knowledge of distributed systems, and

Qualifications

• Hands-on experience with Scala, Spark, and Java.
• Experience building large-scale distributed data processing systems.
• Experience developing and consuming Native APIs and microservices.
• Strong understanding of Spark architecture, performance tuning, and optimization.
• Experience with data modeling, ETL/ELT, and large-scale batch/streaming pipelines.
• Proficient in SQL and relational/non-relational databases.
• Strong debugging, analytical, and problem-solving skills.

Responsibilities

• Design, develop, and optimize large-scale data processing applications using Scala, Spark, and Java.
• Build and maintain high-performance data pipelines processing 80-90 million records daily.
• Develop and integrate Native APIs and data services for analytics platforms.
• Collaborate with cross-functional teams to gather requirements and deliver data solutions.
• Optimize Spark jobs, data workflows, and distributed processes for throughput and latency.
• Implement data quality, monitoring, governance, and operations best practices.
• Troubleshoot performance bottlenecks across pipelines.
• Participate in code reviews and contribute to engineering standards and architecture decisions.

Skills

Scala

Apache Spark

Java

Distributed data processing

Native APIs

SQL

Data modeling

ETL/ELT

Batch and streaming

Tools

Kafka

Airflow

Hadoop ecosystem

CI/CD

DevOps

AWS

Azure

GCP

Job description

Job Description:

About Us

Capco, a Wipro company, is a global technology and management consulting firm. Awarded with Consultancy of the year in the British Bank Award and has been ranked Top 100 Best Companies for Women in India 2022 by Avtar & Seramount. With our presence across 32 cities across globe, we support 100+ clients acrossbanking, financial and Energy sectors. We are recognized for our deep transformation execution and delivery.

WHY JOIN CAPCO?

You will work on engaging projects with the largest international and local banks, insurance companies, payment service providers and other key players in the industry. The projects that will transform the financial services industry.

MAKE AN IMPACT

Innovative thinking, delivery excellence and thought leadership to help our clients transform their business. Together with our clients and industry partners, we deliver disruptive work that is changing energy and financial services.

#BEYOURSELFATWORK

Capco has a tolerant, open culture that values diversity, inclusivity, and creativity.

CAREER ADVANCEMENT

With no forced hierarchy at Capco, everyone has the opportunity to grow as we grow, taking their career into their own hands.

DIVERSITY & INCLUSION

We believe that diversity of people and perspective gives us a competitive advantage.

MAKE AN IMPACT

Job Title: Data Engineer

Position: Data Engineer
Experience: 4+ Years
Work Mode: Hybrid (Capco Office)

Key Responsibilities

• Design, develop, and optimize large-scale data processing applications using Scala, Apache Spark, and Java.
• Build and maintain high-performance data pipelines capable of processing 80-90 million records daily with a focus on scalability, reliability, and efficiency.
• Develop and integrate Native APIs and data services to support business-critical applications and analytics platforms.
• Collaborate with cross-functional teams to gather requirements and deliver robust data engineering solutions.
• Optimize Spark jobs, data workflows, and distributed computing processes to ensure high throughput and low latency.
• Implement best practices for data quality, monitoring, governance, and operational excellence.
• Troubleshoot and resolve performance bottlenecks across data processing and ingestion pipelines.
• Participate in code reviews and contribute to engineering standards, architecture decisions, and continuous improvement initiatives.

Required Skills & Experience

• Strong hands-on experience in Scala, Apache Spark, and Java.
• Proven experience building and supporting large-scale distributed data processing systems handling tens of millions of records daily.
• Expertise in developing and consuming Native APIs and microservices.
• Strong understanding of Spark architecture, performance tuning, partitioning, caching, and optimization techniques.
• Experience with data modeling, ETL/ELT processes, and large-scale batch and streaming data pipelines.
• Solid understanding of distributed systems, concurrency, and high-volume data processing.
• Experience with SQL and relational/non-relational databases.
• Strong debugging, analytical, and problem-solving skills.

Preferred Qualifications

• Experience working in enterprise-scale data environments within Financial Services, Payments, or FinTech domains.
• Exposure to cloud platforms (AWS, Azure, or GCP) and containerized deployments.
• Familiarity with Kafka, Airflow, Hadoop ecosystem, or similar big data technologies.
• Experience with CI/CD pipelines, DevOps practices, and Agile methodologies.

What Were Looking For

• Engineers passionate about solving complex data challenges at scale.
• Professionals who can design highly performant systems capable of processing and transforming massive datasets efficiently.
• Team players who thrive in fast-paced, collaborative environments and take ownership of end-to-end delivery.