Senior Data Platform Engineer

Own and evolve the Data Platform infrastructure across on-premises Kubernetes and cloud environments (AWS, Snowflake), managed as code, and make architectural decisions that keep the platform scalable and portable, avoiding unnecessary vendor lock-in.

Build self-service tooling and CI/CD workflows for data code and infrastructure so Data Engineers and Analytics Engineers can independently create, test and deploy pipelines and dbt projects, with separate environments, automated testing and safe data publishing practices such as Write-Audit-Publish.

Operate core platform services such as orchestration, streaming, query engines and storage, including upgrades, backup and recovery, and incident response.

Set up platform-wide observability through metrics, logs, and alerts, supported by actionable runbooks, to detect issues early and minimize their impact on data consumers.

Manage access control, secrets, and security across the platform, following least-privilege principles, the company’s security standards and best practices, and applicable compliance requirements.

Own capacity planning, performance tuning, and cost optimization across Kubernetes, Snowflake, cloud infrastructure, and tooling; make resource usage and costs visible per workload and use case, and keep the platform efficient as it grows.

Support AI use cases by enabling secure, governed data access for AI assistants and agents (e.g., through MCP servers) and providing infrastructure for services using commercial model APIs or self-hosted open-source models.

Strong hands-on experience in platform engineering, DevOps or SRE, operating production infrastructure for data platforms or backend systems.

Solid experience operating Kubernetes in production, including deployment management, autoscaling, networking, and troubleshooting.

Hands-on experience with infrastructure as code (e.g., Terraform) and CI/CD pipelines (e.g., GitHub Actions, GitLab CI), along with familiarity with GitOps practices.

Practical experience with cloud infrastructure, including networking, IAM, and cost management, preferably on AWS.

Experience setting up monitoring and alerting (e.g., Prometheus, Grafana), troubleshooting performance issues, and responding to production incidents.

Good programming skills in Python or Go for automation and internal tooling.

An understanding of data workloads and tools such as orchestration systems, data warehouses, streaming platforms, and dbt, and how their needs influence platform reliability, performance, and cost.

A practical understanding of infrastructure security, including access control, secrets management and least-privilege principles.

Comfortable using AI-assisted development tools in day-to-day engineering work and sharing effective practices with the team.

A strong sense of ownership, with the ability to work independently, make pragmatic technical trade-offs and collaborate closely with Data Engineers, Analytics Engineers and the Infrastructure team.

Deep hands-on experience operating and scaling streaming platforms and processing frameworks such as Kafka and Apache Flink, particularly for user behavior tracking—from handling traffic spikes at event collection endpoints to ensuring reliable downstream event processing and delivery. This includes managing consumer lag, backpressure, state, checkpointing, and replay strategies.

Experience operating Apache Iceberg in production, including catalog administration and integration with query engines. Understands how partitioning, file compaction, snapshot and metadata maintenance, and update patterns affect performance and storage costs.

Experience designing and operating hybrid data platforms across on-premises and cloud environments, making informed decisions about where data is stored and processed to minimize unnecessary transfers and egress costs. Familiarity with incremental replication, CDC and upsert patterns, and their implications for network usage, latency and consistency would be particularly valuable.