Specialist - Data Platform Engineering

We are looking for a Data Platform Engineer (IC2) to build and operate scalable, reliable, and high-performance data platforms. In this role, you will develop streaming and batch data pipelines, manage Databricks environments, and support our event-driven data infrastructure using Kafka and Kafka Connect. This is an excellent opportunity for engineers with a strong foundation in distributed data processing who want to work on modern data engineering technologies at scale.

Responsibilities

Develop, maintain, and optimize batch and streaming data pipelines using Apache Spark and Apache Flink; Build scalable ETL/ELT workflows for processing large datasets; Manage and maintain Databricks workspaces, clusters, jobs, notebooks, and workflows; Monitor, troubleshoot, and optimize Databricks jobs for performance and cost efficiency; Work with Apache Kafka for event-driven data processing and streaming applications; Configure, deploy, and troubleshoot Kafka Connect connectors for integrating various data sources and sinks; Ensure data reliability through monitoring, alerting, and operational best practices; Collaborate with software engineers, platform engineers, and data consumers to design robust data solutions; Participate in production support, incident response, and root cause analysis; Contribute to automation, documentation, and continuous improvement of the data platform

Requirements

Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience; 3–4 years of experience in software engineering or data engineering; Strong programming skills in Java, Scala, or Python; Hands-on experience with Apache Spark for large-scale data processing; Knowledge of Apache Flink for stream processing; Experience working with Databricks, including cluster management and job orchestration; Good understanding of Apache Kafka, including topics, partitions, producers, and consumers; Familiarity with Kafka Connect and connector deployment; Understanding of distributed systems, data pipelines, and streaming architectures; Experience with Git and CI/CD practices; Strong problem-solving and debugging skills

Nice to have

Experience with Delta Lake and Lakehouse architecture; Knowledge of cloud platforms such as AWS, Azure, or GCP; Familiarity with Airflow, Argo Workflows, or similar orchestration tools; Experience with monitoring tools such as Prometheus and Grafana; Understanding of Kubernetes and containerized deployments; Knowledge of SQL optimization and data modeling