Looking for data Engineer with Flink and Kafka

Absolutely. Below is a client-ready JD for a Data Engineer – Apache Flink & Kafka supporting Wells Fargo. I’d position it as a real-time/streaming data engineering role, rather than a generic Data Engineer opening.

Data Engineer – Apache Flink & Kafka

Client: Wells Fargo
Role: Senior Data Engineer – Flink & Kafka
Domain: Banking / Financial Services
Experience: 6+ Years
Location: Hybrid / Client location as applicable
Employment Type: Contract / Full-Time

Job Summary

We are seeking a strong Data Engineer with hands-on Apache Flink and Apache Kafka experience to design, build, and support high-volume, real-time data processing solutions within Wells Fargo’s enterprise data ecosystem.

The ideal candidate will have deep experience developing streaming data pipelines, event-driven architectures, Kafka-based integrations, and Flink applications handling large-scale transactional and operational datasets.

This role requires strong engineering fundamentals, SQL proficiency, production troubleshooting skills, and experience working within highly regulated financial-services environments.

Key Responsibilities

• Design and develop scalable real-time streaming pipelines using Apache Flink and Apache Kafka.
• Build event-driven data-processing solutions supporting high-volume banking and financial transactions.
• Develop and optimize Flink jobs for stateful stream processing, windowing, joins, aggregations, event-time processing, and complex transformations.
• Build and manage Kafka producers, consumers, topics, partitions, schemas, and consumer groups.
• Implement reliable Kafka-to-Flink and Flink-to-downstream-system integration patterns.
• Develop reusable frameworks for real-time data ingestion, enrichment, transformation, validation, and delivery.
• Implement fault-tolerant processing using Flink checkpointing, savepoints, state management, and recovery mechanisms.
• Ensure appropriate handling of exactly-once / at-least-once processing semantics based on application requirements.
• Develop data-quality validation, reconciliation, monitoring, and exception-handling frameworks.
• Perform performance tuning of Flink applications, Kafka clusters, SQL queries, and data-processing workflows.
• Troubleshoot production issues involving message lag, throughput, partitioning, checkpoint failures, backpressure, data loss, and pipeline latency.
• Work closely with application, architecture, DevOps, infrastructure, database, and business teams.
• Participate in design reviews, code reviews, deployment activities, production support, and root-cause analysis.
• Build monitoring and alerting around streaming applications and Kafka infrastructure.
• Support secure handling of sensitive banking and customer data.
• Contribute to CI/CD automation and deployment of streaming applications across development, QA, UAT, and production environments.