Streaming Data Engineer

As part of the Reply group, Data Reply offers a wide range of services that support customers in becoming data driven. They operate in various industries and business areas, working intensively with customers to achieve meaningful results through effective use of data. Data Reply has many years of experience in transformation projects to achieve "data-driven companies". They focus on the development of data platforms, machine learning solutions, and streaming applications that are automated, efficient, scalable, and secure.

• Bachelor’s or master’s degree in Computer Science, 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 Python, Java, or Kotlin
• Experience with public cloud providers AWS and Azure
• Knowledge of SQL and NoSQL databases
• Familiarity with big data frameworks and tools like Spark and 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 provide strategic guidance
• Strong interpersonal and communication skills
• Very good knowledge of English and Polish
• Strong problem-solving skills and ability to propose creative, efficient solutions
• Nice to have experience with Databricks and 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 including 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 issues affecting 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 to transform data into actionable insights

Client Interaction:

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