Kafka/Spark Software Development Lead

CGI•Pittsburgh, PA

3d•Onsite

About The Position

CGI is looking for an experienced Kafka and Spark Software Development Lead to join our Applications Development and Maintenance team, supporting our client which is a large US Bank, working in an advanced technology environment. This role will require someone at our client site 5 days a week in Pittsburgh, PA.

Requirements

At least 8+ years of experience in designing, developing, and supporting large-scale distributed data processing and streaming applications.
Strong hands-on experience with Apache Kafka, topic configuration, producer/consumer development, Kafka Connect, and Schema Registry.
Extensive experience developing real-time data processing applications using Apache Spark Streaming and/or Spark Structured Streaming.
Proficiency in Java, Scala, or Python (PySpark) with strong object-oriented programming and software development skills.
Strong understanding of distributed systems, message-oriented middleware, data partitioning, fault tolerance, and scalability concepts.
Experience integrating Kafka and Spark with relational databases, NoSQL databases, cloud storage platforms, and enterprise applications.
Strong analytical, troubleshooting, and performance tuning skills in distributed streaming environments.
Excellent communication, collaboration, and stakeholder management skills, with the ability to work effectively in Agile/Scrum teams.
Experience working in Agile development environments with strong collaboration, technical leadership, problem-solving, and stakeholder communication skills.

Nice To Haves

Hadoop Ecosystem (HDFS)
Oracle
Postgre SQL

Responsibilities

As a Kafka Spark Software development Lead, you will be responsible for designing and developing scalable real-time data streaming solutions using Apache Kafka and Spark Structured Streaming to support enterprise-wide data processing and analytics initiatives.
Build and enhance Kafka producers, consumers, topics, and event-driven architectures to enable reliable, high-throughput data ingestion across multiple platforms.
Develop and optimize Spark Streaming applications for real-time data transformation, aggregation, enrichment, and analytics to meet evolving business requirements.
Integrate Kafka and Spark with data lakes, data warehouses, databases, APIs, and enterprise applications to facilitate seamless data movement and processing.
Implement and maintain highly available streaming pipelines using checkpointing, replication, schema management, and recovery mechanisms.
Monitor, troubleshoot, and tune Kafka and Spark streaming jobs to improve performance, scalability, and operational reliability.
Collaborate with architects, data engineers, DevOps teams, and business stakeholders to design and implement modern streaming and event-driven data platforms.
Analyzing user requirements, and defines technical project scope and assumptions for assigned tasks.
Creating technical designs for new systems, and/or modifications to existing systems.
Translating detailed requirements into functional system designs.
Prioritizing work, meeting deadline and also establishing and maintaining effective working relationships with clients, project team members, supervisors, and employees from other departments.
Partner with business leaders, enterprise architects, and product owners to identify new graph-based use cases, evaluate emerging technologies, and align Neo4j initiatives with digital transformation goals.