Senior Data Platform Engineer

Main Responsibilities

• Design, deploy, and operate the production streaming platform on Kubernetes — cluster lifecycle, topics, partitioning and retention strategy, capacity planning, and upgrades.
• Build and maintain change-data-capture and connector pipelines moving data between operational databases, the streaming layer, and analytical stores.
• Own schema governance — a schema registry, compatibility rules, and versioning discipline so producer changes never silently break consumers.
• Develop custom connectors, transformations, and stream-processing logic in Java or Go where off-the-shelf components fall short.
• Build self-service onboarding so product teams can provision topics, schemas, and access through infrastructure as code rather than tickets.
• Automate the platform with Terraform and GitOps — no manually configured clusters, no undocumented topic definitions.
• Instrument the pipeline for production: metrics, lag and throughput monitoring, alerting, and dashboards that both the platform team and application owners can act on.
• Enforce security and multi-tenant isolation across the streaming layer — mTLS, authentication, topic-level RBAC, encryption, and audit trails.
• Guarantee delivery semantics that the business can rely on — ordering, idempotency, exactly-once where required, replay, and disaster recovery.
• Partner with backend and SRE teams on event schema design, producer patterns, and failure modes; participate in on-call for the platform you own.
• Mentor engineers on streaming architecture and raise the team's bar through design review and documentation.

Required Skills/Abilities

• 10+ years in software, data, or platform engineering, including deep hands-on ownership of a production event-streaming platform.
• Expert-level Apache Kafka (or Confluent Platform/Cloud) — administration, tuning, troubleshooting, and capacity management at production scale.
• Strong Kafka Connect and CDC experience — Debezium, JDBC, and custom connector or transformation development.
• Proven schema-registry practice and compatibility management across many independent producers and consumers.
• Solid programming ability in Java, Python, or Go — enough to build connectors, transformations, and platform tooling, not only configure them.
• Strong SQL and relational database knowledge (PostgreSQL, MySQL, or equivalent), including replication and CDC mechanics.
• Kubernetes fluency — deploying, operating, and debugging stateful workloads.
• Infrastructure as code discipline (Terraform or equivalent) and CI/CD pipeline ownership.
• Clear written English for design docs, runbooks, and asynchronous review across global time zones.

Must Have

• Streaming: Apache Kafka or Confluent, Kafka Connect, Schema Registry, and stream processing.
• Data Movement: CDC tooling (Debezium or equivalent), connector development, and pipelines into analytical stores.
• Kubernetes Native: Kubernetes, Docker, and Helm-packaged workloads in production.
• Automation: Terraform, CI/CD (Jenkins, GitHub Actions, or equivalent), and GitOps practice.
• Data Stores: PostgreSQL at scale, plus at least one cloud data warehouse or search store (Snowflake, BigQuery, Elasticsearch, or equivalent).
• Cloud: AWS or GCP — networking, IAM, managed Kubernetes.
• Security & Observability: mTLS, OIDC/SSO, RBAC, and Prometheus/Grafana monitoring.

Nice to Have

Education and Experience

What does Mirantis offer you?