data engineer for CDC platforms

Software Mind develops solutions for companies worldwide and builds cross-functional engineering teams. Its customer provides technology and insights that help organizations manage risk and hire talent through scalable, configurable screening programs.

• 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 and alerting, recovery workflows, and automated testing;
• Integrate CDC streams with 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 flowing through the pipelines, including masking, hashing, access control, and retention.

• Solid commercial experience as a data or platform engineer with hands-on streaming or CDC pipeline work rather than batch reporting alone;
• Practical Kafka knowledge, including topics, partitions, offsets, consumer groups, delivery semantics, and at least one self-operated Kafka Connect deployment;
• Strong SQL and PostgreSQL skills, including WAL, logical replication, replication slots, and replication lag;
• Working understanding of CDC concepts, including initial snapshots, inserts, updates and deletes, event ordering, at-least-once delivery, and schema evolution;
• Confident Python skills for automation and tooling, including orchestration, monitoring, recovery scripts, and automated tests;
• Hands-on experience with Azure data services such as Event Hubs, ADLS, or Azure PostgreSQL;
• Practical Kubernetes experience as a user, including deploying workloads, handling configuration and secrets, reading logs, and debugging failing pods;
• Ability to debug running pipelines from metrics and logs and distinguish throughput problems from backpressure, retries, or genuine connector failures;
• Nice to have: production experience with Debezium, experience 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 leading global clients;
• International business trips;
• Non-corporate atmosphere;
• Language classes;
• Internal and external training;
• Private healthcare and insurance;
• Multisport card;
• Well-being initiatives.