platform engineer in data platforms

Software Mind develops solutions for companies worldwide and builds cross-functional engineering teams for transformative technology projects. Its customer provides scalable technology for risk management and talent screening, supporting more than 33,000 clients worldwide.

• Build and operate change-data-capture pipelines from PostgreSQL into Azure using Kafka Connect and Debezium;
• Configure, deploy, and scale connectors end to end, including connector setup, task management, offsets, schema history, and snapshot strategy;
• Run pipelines as stateful workloads on Kubernetes (AKS), covering configuration, secrets, networking, and resource tuning;
• Monitor and troubleshoot the platform in production, including connector failures, task rebalances, restarts, throughput, backpressure, message-size limits, retries, and recovery;
• Automate the platform in Python through configuration-driven onboarding, pipeline orchestration, monitoring, alerting, recovery workflows, and automated testing;
• Integrate CDC streams with Azure data services, including Event Hubs, ADLS, Azure PostgreSQL, ADF, and Databricks;
• Manage platform infrastructure as code to keep environments reproducible and changes reviewable;
• Apply data protection requirements to sensitive data through masking, hashing, access control, and retention.

• Have solid commercial experience as a data or platform engineer, including hands-on streaming or CDC pipeline work rather than batch reporting alone;
• Have practical Kafka knowledge covering topics, partitions, offsets, consumer groups, and delivery semantics, including at least one self-operated Kafka Connect deployment;
• Have strong SQL and PostgreSQL skills, with working knowledge of WAL, logical replication, replication slots, and replication lag;
• Understand CDC concepts including initial snapshots, inserts, updates and deletes, event ordering, at-least-once delivery, and schema evolution;
• Use Python confidently for automation and tooling, including orchestration, monitoring, recovery scripts, and automated tests;
• Have hands-on experience with Azure data services such as Event Hubs, ADLS, or Azure PostgreSQL;
• Be comfortable using Kubernetes to deploy workloads, handle configuration and secrets, read logs, and debug failing pods;
• Be able to debug running pipelines from metrics and logs, distinguishing throughput problems from backpressure, retries, or connector failures;
• Nice to have: production experience with Debezium, operating stateful workloads on AKS, infrastructure as code and CI/CD with Terraform or Bicep, production-scale Databricks and ADF experience, data protection controls for sensitive data.

• Flexible employment and remote work;
• International projects with global clients;
• International business trips;
• Language classes;
• Internal and external training;
• Private healthcare and insurance;
• Multisport card;
• Well-being initiatives.