Data Platform Engineer
⚲ Warszawa
6 720 - 8 400 USD netto (B2B)
Wymagania
- SQL
- Python
- ETL
- Amazon AWS
- Kubernetes
- Linux
- Snowflake
- Kafka
Opis stanowiska
RITS Group is an IT company offering top-quality programming services for both the Polish and international markets. We are proud to be one of the fastest-growing technology companies. Our priority is the quality and innovation of the solutions we deliver, and our organizational culture is based on trust, collaboration, and continuous improvement.
About the Team
The Data Platform team builds and operates the shared infrastructure that
company's business lines rely on to move, manage, and serve data across the
firm. The team owns the systems on which data engineering is done — it does not
build the business-facing pipelines themselves. You will spend the majority of your
week writing software that other engineers at company depend on.
Responsibilities
• ETL / ELT frameworks. Build the pipeline frameworks, transform libraries, and ingest patterns that company's data engineers rely on every day — the shared code path that every business-facing pipeline runs through.
• Own the abstractions that make the common case correct-by-default: schema handling,idempotency, retries, backfills, and data-quality gating.
• Streaming infrastructure. Operate company's Kafka Connect and Schema
• Registry deployments. Build and maintain connectors; own schema evolution, compatibility policy, and connector lifecycle.
• Orchestration platform. Own the workflow orchestrator (Airflow, Prefect, or Dagster) as a shared multi-tenant service — deployment, upgrades, tenancy, and resource quotas.
• Data warehouse administration. Design and govern the Snowflake environment: role and permission model, cost controls, resource monitors, and workload isolation across teams.
• Observability. Own the observability of the platform: instrument services with metrics, integrate third-party systems into the monitoring stack, and operate Prometheus / Grafana with the alerting rules and SLOs the team runs against. Infrastructure automation.
• Manage infrastructure as code (Terraform) and deliver all changes through CI/CD. Manual console changes in production are not permitted.
• Internal developer tooling. Build the command-line tools, libraries, and templates that engineers use to onboard onto and interact with the platform.
• Production reliability. Investigate incidents, produce postmortems, and convert one-time fixes into permanent controls.
• Occasional off-hours on-call participation is expected.
Required Qualifications
• 5+ years in a platform, site reliability, or infrastructure engineering role, with the majority of the week spent writing software rather than SQL or configuration.
• Strong daily software engineering in Python; working proficiency in a second general-purpose language (Go, Java, or similar) is advantageous.
• Track record of designing and shipping shared frameworks or libraries used by other engineers — for example, ETL / ELT pipeline scaffolding, transformation libraries, ingest patterns, schema-handling utilities, or a client library that standardizes how services talk to Snowflake or Kafka.
• Hands-on experience running or designing several of the following in production: Kafka / Kafka Connect / Schema Registry, Snowflake administration, a workflow orchestrator (Airflow, Prefect, or Dagster), and a monitoring stack (Prometheus, Grafana, or Datadog).
• Direct experience instrumenting application code using a metrics library (Prometheus client, Micrometer, OpenTelemetry, or similar).
• Experience building or extending exporters or custom collectors that surface third-party system state into a monitoring stack.
• Production experience with infrastructure-as-code (Terraform, Pulumi, or CDK).
• Production experience with CI/CD pipelines (GitLab CI, GitHub Actions, Jenkins, or equivalent).
• SQL proficiency and applied knowledge of query optimization sufficient to debug and tune platform-adjacent queries.
• Production experience with AWS, Kubernetes, and Linux.
• Strong written and verbal communication with both technical and non-technical stakeholders.
About the Team
The Data Platform team builds and operates the shared infrastructure that
company's business lines rely on to move, manage, and serve data across the
firm. The team owns the systems on which data engineering is done — it does not
build the business-facing pipelines themselves. You will spend the majority of your
week writing software that other engineers at company depend on.
Responsibilities
• ETL / ELT frameworks. Build the pipeline frameworks, transform libraries, and ingest patterns that company's data engineers rely on every day — the shared code path that every business-facing pipeline runs through.
• Own the abstractions that make the common case correct-by-default: schema handling,idempotency, retries, backfills, and data-quality gating.
• Streaming infrastructure. Operate company's Kafka Connect and Schema
• Registry deployments. Build and maintain connectors; own schema evolution, compatibility policy, and connector lifecycle.
• Orchestration platform. Own the workflow orchestrator (Airflow, Prefect, or Dagster) as a shared multi-tenant service — deployment, upgrades, tenancy, and resource quotas.
• Data warehouse administration. Design and govern the Snowflake environment: role and permission model, cost controls, resource monitors, and workload isolation across teams.
• Observability. Own the observability of the platform: instrument services with metrics, integrate third-party systems into the monitoring stack, and operate Prometheus / Grafana with the alerting rules and SLOs the team runs against. Infrastructure automation.
• Manage infrastructure as code (Terraform) and deliver all changes through CI/CD. Manual console changes in production are not permitted.
• Internal developer tooling. Build the command-line tools, libraries, and templates that engineers use to onboard onto and interact with the platform.
• Production reliability. Investigate incidents, produce postmortems, and convert one-time fixes into permanent controls.
• Occasional off-hours on-call participation is expected.
Required Qualifications
• 5+ years in a platform, site reliability, or infrastructure engineering role, with the majority of the week spent writing software rather than SQL or configuration.
• Strong daily software engineering in Python; working proficiency in a second general-purpose language (Go, Java, or similar) is advantageous.
• Track record of designing and shipping shared frameworks or libraries used by other engineers — for example, ETL / ELT pipeline scaffolding, transformation libraries, ingest patterns, schema-handling utilities, or a client library that standardizes how services talk to Snowflake or Kafka.
• Hands-on experience running or designing several of the following in production: Kafka / Kafka Connect / Schema Registry, Snowflake administration, a workflow orchestrator (Airflow, Prefect, or Dagster), and a monitoring stack (Prometheus, Grafana, or Datadog).
• Direct experience instrumenting application code using a metrics library (Prometheus client, Micrometer, OpenTelemetry, or similar).
• Experience building or extending exporters or custom collectors that surface third-party system state into a monitoring stack.
• Production experience with infrastructure-as-code (Terraform, Pulumi, or CDK).
• Production experience with CI/CD pipelines (GitLab CI, GitHub Actions, Jenkins, or equivalent).
• SQL proficiency and applied knowledge of query optimization sufficient to debug and tune platform-adjacent queries.
• Production experience with AWS, Kubernetes, and Linux.
• Strong written and verbal communication with both technical and non-technical stakeholders.
🔍 Dekoder Ogłoszenia
🔴
We are proud to be one of the fastest-growing technology companies.
Firma szybko rośnie, co może oznaczać zarówno dynamiczne możliwości rozwoju, jak i potencjalny chaos organizacyjny oraz presję na wyniki.
🟡
Our priority is the quality and innovation of the solutions we deliver
Kładą nacisk na jakość i innowacyjność, co może sugerować wysokie standardy techniczne, ale też potencjalnie długie cykle rozwojowe i potrzebę ciągłego uczenia się.
🟡
our organizational culture is based on trust, collaboration, and continuous improvement.
Podkreślają zaufanie i współpracę, co brzmi pozytywnie, ale w praktyce może oznaczać dużą autonomię, która wymaga proaktywności i samodzielności w rozwiązywaniu problemów.
🟡
The Data Platform team builds and operates the shared infrastructure that company's business lines rely on to move, manage, and serve data across the firm. The team owns the systems on which data engineering is done — it does not build the business-facing pipelines themselves.
Rola polega na budowaniu i utrzymaniu infrastruktury dla innych inżynierów danych, co oznacza pracę nad narzędziami i platformami, a nie bezpośrednio nad rozwiązaniami dla biznesu.
🟡
You will spend the majority of your week writing software that other engineers at company depend on.
Głównym zadaniem będzie tworzenie oprogramowania dla innych inżynierów, co może oznaczać mniej bezpośredniego kontaktu z użytkownikiem końcowym i większy nacisk na stabilność oraz skalowalność wewnętrznych narzędzi.