Streaming Data Engineer

As part of the Reply group, Data Reply offers a wide range of services that support customers in becoming data driven. We operate in various industries and business areas and work intensively with our customers so that they can achieve meaningful results through the effective use of data. Data Reply offers many years of experience in transformation projects to achieve “data-driven companies”. We focus on the development of data platforms, machine learning solutions and streaming applications - automated, efficient, and scalable - without making any compromises in IT security.

• Bachelor’s or master’s degree in CS, Engineering, IT, or a related field
• Knowledge of the Kafka ecosystem (Kafka clusters, connectors and clients, Kafka Streams) and other streaming platforms (e.g. Flink)
• Strong programming skills in languages such as Python, Java or Kotlin
• Experience with public cloud providers AWS/Azure
• Knowledge of SQL and NoSQL databases
• Familiarity with big data frameworks and tools like Spark, formats such as Apache Iceberg
• Experience in Docker, familiarity with Kubernetes is nice to have
• Familiarity with data modeling and warehousing techniques
• Strong analytical skills to analyze complex data structures and derive insights
• Strong interpersonal and communication skills to collaborate with team members and clients
• Very good knowledge of English and Polish
• Strong problem-solving skills and ability to propose creative, efficient solutions

Nice to have:

• Experience with Databricks, DBT
• Availability to work in a hybrid mode with at least 2 visits to the office per month

Streaming Data Processing:

• Design, implement, test and maintain highly scalable real-time stateless and stateful data transformations, ensuring low latency and data quality

Data System Design:

• Design and implement robust, scalable data processing systems: this involves selecting appropriate storage technologies, designing schemas, and planning integration strategies

Data Integration and ETL Development:

• Develop and maintain pipelines for data transformation, integration, and ETL processes. Ensure data quality and accessibility

Performance Optimization:

• Monitor, tune, and optimize data applications and database performance. Address any issues that may affect data processing speeds or analytics capabilities

Consulting and Strategy:

• Provide expert advice and consultancy services to clients on data strategies, architecture choices, and technological advancements

Analytics and Business Intelligence Support:

• Assist in developing analytics platforms and business intelligence solutions, ensuring that data can be effectively transformed into actionable insights

Client Interaction:

• Work closely with clients to understand their business needs and technical requirements. Translate these requirements into effective data engineering solutions