Senior Data Platform Engineer

• We are looking for a Senior Data Platform Engineer to join the Data Platform at Smartly!
• Smartly is building the standard path for governed data sharing across product services, analytics, and AI/ML
• The Data Platform team owns the contracts, control mechanisms, publication paths, catalog metadata, lifecycle rules, lineage, permissions, and runtime building blocks that make reusable data safe and practical at scale
• This is a role for someone who wants to shape a platform early, while working on concrete production problems from the start
• Smartly already has the production systems, Kafka backbone, PostgreSQL databases, analytical consumers, and AI/ML use cases that need a better data foundation
• The challenge is to turn that reality into a standard path that engineers actually want to use
• Our first milestone is focused: centrally managed publishing tables in service-owned PostgreSQL databases, captured through Debezium CDC into Kafka, and registered in DataHub with ownership, lifecycle, lineage, and impact-analysis metadata
• Longer-term, the platform expands toward self-service data products, governed materialisations, durable shared state, and reusable data across Smartly’s operational and analytical systems
• This is not a role where you only maintain an existing reporting pipeline
• You will help design and build the platform path that other engineering teams use when they publish and consume shared data
• 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

Benefits

• Career development opportunities, bonus programs and annual performance reviews
• Hybrid working model, with the option to work abroad for up to 30 days
• Company equity options program
• Paid holidays, OOO time and family leaves
• Generous healthcare packages and mental health services

• 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