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Endava

Global tech advisory for private equity

Senior Data Engineer - Real-Time Data Processing & Streaming

Data EngineeringConfirmed live in the last 24 hours
Full-timeTârgu Mureș, Romania

About the Job

Requirements

  • Strong professional experience in Data Engineering focused on streaming data processing and distributed systems.
  • Advanced programming skills in Java 11 or later, including experience with the Spring framework.
  • Hands-on experience with Apache Kafka, including Kafka Streams, Kafka Connect, and development of Kafka-based integrations.
  • Practical experience with streaming and distributed processing frameworks such as Apache Flink, Apache Spark, or Spark Streaming.
  • Solid understanding of event-driven architectures, asynchronous communication patterns, and messaging systems, including AMQP.
  • Experience designing and implementing data transformation, enrichment, and integration pipelines.
  • Experience developing and integrating microservices and distributed applications.
  • Hands-on experience with AWS cloud services, particularly S3, SQS, and EKS.
  • Experience deploying and operating containerized applications using Docker and Kubernetes.
  • Working knowledge of MongoDB, including collections, document structures, and Java-based data access.
  • Familiarity with Redis and its use in distributed applications.
  • Experience with Git-based development workflows and CI/CD pipelines.
  • Strong troubleshooting and problem-solving skills in distributed, high-throughput data processing environments.
  • Ability to collaborate with technical leads and cross-functional engineering teams while taking ownership of implementation and delivery.

Responsibilities

  • Design, develop, and maintain scalable real-time and near-real-time data processing pipelines.
  • Implement streaming data processing solutions using technologies such as Apache Kafka, Apache Flink, and Apache Spark.
  • Develop and maintain Java-based services and integration components for processing data received from physical devices.
  • Implement complex data transformations, mappings, validations, and enrichments to support business and technical requirements.
  • Build and maintain event-driven integrations using Kafka, Kafka Streams, Kafka Connect, and messaging protocols such as AMQP.
  • Ensure reliable data delivery to downstream consumers, applications, and storage systems.
  • Implement and maintain MongoDB collections, data structures, and related application-level integrations following architectural guidelines.
  • Develop, deploy, and maintain cloud-native services primarily on AWS, leveraging Kubernetes and managed cloud services.
  • Contribute to modernization and migration of legacy data processing components toward Kafka-based streaming architectures.
  • Collaborate with technical leads and engineering teams to translate architectural designs into robust, production-ready implementations.
  • Troubleshoot performance, reliability, and data consistency issues across distributed data processing systems.
  • Contribute to code quality, automated testing, CI/CD practices, and continuous improvement of engineering processes.

Desired Qualifications

  • Experience with Apache Pulsar or Apache Storm, particularly in legacy system maintenance or modernization initiatives.
  • Familiarity with Microsoft Azure services and hybrid or multi-cloud environments.
  • Experience processing telemetry, IoT, or other event data generated by physical devices.
  • Knowledge of SMTP-based integrations.
  • Experience migrating legacy messaging and stream processing solutions to modern Kafka-based architectures.
  • Understanding of performance optimization, fault tolerance, monitoring, and observability in distributed streaming systems.

About the Company

Endava

Global tech advisory for private equity

Endava is a global technology company that provides digital and technology advisory services to private equity clients. It combines private-equity expertise with its capability to design and build scalable technology platforms, offering actionable analysis across the investment life-cycle. Their teams blend product strategy, technology strategy, intelligent experiences, and high-quality engineering to help clients become more engaging, responsive, and efficient. Revenue comes from advisory fees charged for projects or ongoing services, rather than selling a product. The company differentiates itself by merging deep private-equity know-how (from Intuitus Advisory) with strong technology delivery and product design, delivering end-to-end advice and technical execution across a global footprint. The goal is to help private equity firms maximize the value of their investments by providing board-level insights and technology-driven strategies throughout the investment process.

Company size
5,001-10,000
Company stage
IPO
Headquarters
London, United Kingdom
Founded
2000

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