Position: Senior Data engineer
Company: Raiffeisen Bank
Category: Data Science
Experience required: 3y
English level: intermediate

Skills and competences

Must have
- experience in Data Engineering / DB Development / ETL Development (3+ years)
- Hands-on experience with CDC, transactional & high loaded financial\payment systems & payments processing
- expert knowledge of Python, SQL, PL/SQL
- Practical experience with RDBMS (Oracle, PostgreSQL)
- Practical experience with NoSQL databases
- experience in Big Data stack (Hadoop, HBase, Kafka, Spark)
- Hands-on experience with Kafka and Kafka Streams
- Experience in Java (ver.8+ required; ideally,13)
- Practical experience for various environments with:
- Linux OS (RH, Centos, Debian, etc. );
- K8s cluster: Docker (+ registry); Prometheus, Grafana; EFK stack: ElasticSearch, FluentD, Kibana; Jaeger; Redis as a microservices’ sidecar; Helm; Node exporter; Kafka exporter; Postgresql exporter; Docker image for openjdk; Calico; Ingress Nginx Controller for Kubernetes); PostgreSQL with Patroni (HA) ; Kafka cluster (manager) ;
- CI/CD server (Jenkins, Gitlab, Nexus, SonarQube); Infrastructure provisioning (TerraForm, Terragrunt);
- Components Orchestration (Ansible); Security (Hashicorp Vault)
- Other FWs (Swagger, SpringBoot, etc.)
- Understanding of Agile/Scrum.
- good communication skills (English intermediate+) — verbal and written

Nice to have
- experience in Apache NiFi
- knowledge of Jenkins and CI/CD principles, experience in pipelines development
- experience in AWS (S3, Glue, Athena, EMR, Redshift)
- knowledge and experience in Data Science / Machine Learning
- experience with processing of large amount of data
- Deep understanding of microservices architectural principles
- Pulsar, Debezium (CDC), Flink (streaming);
- опыт с OLAP DB experience (Greenplum , ClickHouse, etc.) ;
- Airflow (pipelines) ;
- PowerBI / Superset (visualization).
- analytics and design skills
- experience in Jira and Confluence
- understanding of microservices architectural principles

Responsibilities
- design and development of data processing pipelines for Payments solutions
- building cloud native deliveries for on-premise Kubernetes cluster or AWS deployment
- interacting with stakeholders for requirements elicitation
- research and prototyping of promising tools/approaches/practices with further implementation
- managing knowledge base / technical documentation for developed solutions
- participating in Enterprise Data Platform design and development

Expectations
- passion for data
- good team player
- fast learner and adaptable to changing environment
- result oriented and proactive
- problem solving skills

Project info

We develop an automated and flexibly adjustable microservices based systems. One of them (CM service) that will:
- provide Integration Wall street system with new CM service
- ensure STP and eliminate/minimize manual work
- increase transaction processing capacity and speed up processing time
- allow for quick adaptation of new products (e.g. derivatives)
- allow for automated real-time position keeping, mark-to-market, revaluation of portfolios and limit control

CM service to be onboarded onto platform for Developers and QA with SRE practices. The platform must have Kubernetes, Observability, CI/CD, secret management, traffic management, etc. We want to build a hybrid cloud. Focus on open source.

Our stack:
- OS: Linux (RHEL based)
- Orchestration: Kubernetes
- Development stack: Java based microservices (Spring boot and not only)
- Observability: Prometheus, Grafana, Zabbix,Appdynamics, EFK,Opsgenie, Jaeger
- CI/CD:GitlabCI, Jenkins (imperative pipelines)
- IaCand Config management: Ansible, Terraform
- Message broker: Kafka, IBM MQ
- Database: Postgres, Oracle DB(plus integration with otherdata sources)
- NoSQL: Redis, MongoDB