Data Engineer with Kafka

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 across banking, 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.

Job Title: Data Engineer with Kafka

Position: Data Engineer with Kafka
Experience: 5+ Years
Work Mode: Hybrid (Capco Office)

Job Summary

We are looking for an experienced Senior Data Engineer with strong expertise in Apache Kafka, Apache Spark, Scala/PySpark, SQL, and modern data engineering technologies . The ideal candidate should have hands-on experience designing, developing, optimizing, and maintaining scalable data pipelines, including batch and real-time streaming solutions.

The role requires strong technical expertise, problem-solving abilities, production support experience, and the capability to independently deliver complex data engineering solutions while mentoring junior team members.

Key Responsibilities

Design, develop, and maintain scalable, reliable, and high-performance data pipelines for batch and real-time data processing.
Implement and optimize Kafka-based event streaming solutions , ensuring reliable data ingestion, processing, and integration.
Develop production-grade data processing applications using Apache Spark and Scala/PySpark .
Optimize Spark workloads through effective partitioning, caching, skew handling, join optimization, and performance tuning.
Write advanced SQL queries involving complex joins, window functions, data transformations, and query optimization.
Design, develop, and troubleshoot ETL/ELT workflows using Apache NiFi, Apache Airflow, or Azure Data Factory (ADF) .
Work with cloud-based data storage solutions such as Amazon S3, Azure Data Lake Storage (ADLS), and Data Lakes .
Develop Python scripts, automation utilities, reusable ETL components, and testing frameworks.
Monitor and support production data pipelines, troubleshoot incidents, perform root cause analysis (RCA), and implement preventive measures.
Design end-to-end data engineering solutions with a focus on scalability, performance, reliability, and maintainability.
Conduct code reviews, enforce engineering best practices, and mentor junior data engineers.

Required Technical Skills

1. Apache Kafka / Event Streaming

Strong understanding of Kafka architecture, event streaming concepts, and implementation.
Hands-on experience developing and supporting Kafka-based data ingestion and streaming pipelines.
Knowledge of Kafka producers, consumers, topics, partitions, consumer groups, and offset management.
Understanding of fault tolerance, message delivery guarantees, and streaming performance optimization.

2. Apache Spark

Strong hands-on experience with Apache Spark for large-scale data processing.
Expertise in Spark performance tuning, including joins, partitioning, caching, data skew handling, and troubleshooting.
Experience developing and optimizing production-grade Spark applications.

3. Scala / PySpark

Strong proficiency in at least one of Scala or PySpark, with working knowledge of the other.
Ability to write efficient, maintainable, and production-ready code.

4. SQL

Advanced SQL skills, including complex joins, window functions, query optimization, and data modeling.
Experience working with large datasets and optimizing SQL performance.

5. Data Orchestration – NiFi / Airflow / ADF

Strong experience in at least one orchestration tool, with working knowledge of the others.
Ability to independently design, develop, monitor, and troubleshoot data pipelines.

6. Cloud Storage – S3 / ADLS / Data Lake

Experience working with cloud-based data lakes and storage solutions.
Understanding of file formats, partitioning strategies, storage design, data lifecycle management, and performance considerations.

7. Python

Good Python scripting and automation skills.
Experience building utilities, ETL components, and automated testing frameworks.

8. Production Support & Troubleshooting

Experience handling production incidents, monitoring data pipelines, and performing root cause analysis.
Ability to implement resiliency improvements and ensure pipeline stability.

Additional Expectations

Ability to independently design and deliver medium-to-complex data engineering pipelines with minimal guidance.
Strong analytical, debugging, and problem-solving skills.
Good understanding of data engineering best practices, performance optimization, and scalable architecture.
Experience conducting code reviews and mentoring junior engineers.
Strong communication and collaboration skills.

Qualifications

Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, or a related discipline.
Minimum 5+ years of relevant experience in data engineering, big data technologies, and data pipeline development.
Hands-on experience with Kafka-based streaming solutions and distributed data processing frameworks.

Preferred Candidate Profile

We are seeking a technically strong, hands-on Senior Data Engineer who can independently own data engineering deliverables, build robust Kafka-based streaming pipelines, optimize large-scale Spark workloads, resolve complex production issues, and contribute to technical design decisions.

If you are keen to join us, you will be part of an organization that values your contributions, recognizes your potential, and provides ample opportunities for growth. For more information, visit Follow us on Twitter, Facebook, LinkedIn, and YouTube.

Data Engineer with Kafka

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