Senior Data Platform Engineer

Join Smartly, a leading company in the field of data sharing and analytics. As a Senior Data Platform Engineer, you will play a crucial role in shaping the data platform and addressing concrete production problems. You will design and build platform services, tools, and workflows for data publishing and consumption, and work with various teams to make the standard path easier and safer. This is an exciting opportunity to make a significant impact in the field of data engineering.

• Design and build platform services, tools, and workflows for contract-driven data publishing and consumption.
• Build the first production path for platform-managed PostgreSQL publishing tables, Debezium CDC, Kafka topics, and catalog registration.
• Operate and improve the CDC runtime model, including Kubernetes workloads, source-specific isolation, offsets, schema history, heartbeats, signal channels, lag visibility, replay, resnapshotting, and recovery.
• Practical experience with PostgreSQL or other relational databases, including schema design, migrations, transactions, replication, or operational performance
• Experience with Kafka, event streaming, CDC, Debezium, or adjacent data-movement technologies. Direct Debezium experience is valuable, but strong fundamentals matter more
• Ability to turn ambiguous architectural direction into practical increments, make trade-offs explicit, and deliver useful production systems without waiting for perfect certainty
• Experience building software that other engineers use: CLIs, internal platforms, control planes, automation, CI validation, developer tooling, or self-service workflows
• Familiarity with Kubernetes and cloud or infrastructure platforms, including runtime isolation, deployment, observability, and operational ownership
• Strong experience building and operating production software systems, preferably in platform, infrastructure, backend, or data-intensive environments
• Clear written and spoken communication in English. This role involves aligning multiple engineering teams around standards, contracts, and ownership boundaries
• Deep understanding of distributed systems trade-offs: reliability, idempotency, replay, eventual consistency, ownership boundaries, compatibility, and operational failure modes
• Open table formats such as Apache Iceberg, object-store-backed data lakes, S3, BigQuery, ClickHouse, or analytical storage systems
• DataHub or other data catalog, lineage, or governance systems
• GitHub Actions, policy-as-code, infrastructure-as-code, controllers, operators, or reconciliation-based systems
• Experience supporting analytics, AI/ML, feature-store-style access patterns, or cross-domain product-service integrations
• Data contracts, schema registries, Protobuf, Avro, JSON Schema, compatibility checking, field-level metadata, or contract testing
• Experience with JVM languages and Python, TypeScript, Go, or Ruby