What you will do

• 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.
• Create GitOps-style workflows where contracts, schemas, ownership, lifecycle, compatibility, policy metadata, and runtime desired state are reviewed, validated, and reconciled as code.
• Build tooling that gives product engineers fast feedback before they publish or change data products.
• Help define how Smartly models data products, publishing contracts, schema evolution, field stability, deletion semantics, access policy, lineage, and cost attribution.
• 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.
• Integrate platform metadata into DataHub so engineers can discover data products, understand ownership, trace lineage, and reason about the impact of changes.
• Work with product, infrastructure, data engineering, analytics, and AI/ML teams to make the standard path easier and safer than bespoke point-to-point integrations.

What we are looking for

• Strong experience building and operating production software systems, preferably in platform, infrastructure, backend, or data-intensive environments.
• Deep understanding of distributed systems trade-offs: reliability, idempotency, replay, eventual consistency, ownership boundaries, compatibility, and operational failure modes.
• 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.
• 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.
• Ability to turn ambiguous architectural direction into practical increments, make trade-offs explicit, and deliver useful production systems without waiting for perfect certainty.
• Clear written and spoken communication in English. This role involves aligning multiple engineering teams around standards, contracts, and ownership boundaries.

Useful experience, but not required

• DataHub or other data catalog, lineage, or governance systems.
• Open table formats such as Apache Iceberg, object-store-backed data lakes, S3, BigQuery, ClickHouse, or analytical storage systems.
• GitHub Actions, policy-as-code, infrastructure-as-code, controllers, operators, or reconciliation-based systems.
• Data contracts, schema registries, Protobuf, Avro, JSON Schema, compatibility checking, field-level metadata, or contract testing.
• Experience supporting analytics, AI/ML, feature-store-style access patterns, or cross-domain product-service integrations.
• Experience with JVM languages and Python, TypeScript, Go, or Ruby.