Strong Middle Data Engineer

Jira ticket

We are expanding our team and looking for a Data Engineer to help us build a scalable, reliable data architecture.

In this role, you will build reliable data pipelines, design clear domain models, and optimize our data warehouse. We value a practical, problem-solving mindset — someone who easily navigates ambiguous requirements, brings structure to complex tasks, and takes ownership of their solutions.

In this role, you will:

• Build and support Airflow pipelines to ingest data from third-party sources (APIs, payment providers, ad networks) with retries, backfills, and quality checks.
• Work directly with Analytics Engineers and product teams to translate ambiguous business requests into reliable data models.
• Design and implement fact and dimension tables in dbt using dimensional modeling principles.
• Migrate existing SQL transformations into dbt models, adding tests and documentation.
• Refactor the current dbt project: improve layer structure, standardize naming conventions, and set up CI checks.
• Optimize slow or costly warehouse queries and materializations.
• Evaluate table formats and set up initial ingestion flows for our Data Lakehouse prototype.

Skills you’ll need to bring:

• Strong proficiency in Python and advanced SQL, including complex transformations, window functions, and query optimization.
• Proven experience building and orchestrating ELT/ETL pipelines using Airflow.
• Solid experience with dbt for data modeling, testing, documentation, and managing project structure.
• Good understanding of dimensional modeling concepts, Kimball methodology, star/snowflake schemas, and SCDs.
• Practical experience working with cloud data warehouses (preferably BigQuery, Redshift, or Snowflake) and Cloud Storage.
• Hands-on experience with Docker, Git workflows, and basic cloud/containerized environments.
• A problem-solving mindset with the ability to handle ambiguous requirements, communicate effectively with stakeholders, and use AI/LLM tools (like Claude) to speed up delivery.
• At least an Intermediate level of English and fluent Ukrainian.

As a plus:

• Experience with change data capture (CDC) tools and patterns (e.g., Debezium).
• Background in building and optimizing large-scale data processing jobs with PySpark.
• Experience with real-time data processing using message brokers like Kafka or RabbitMQ.
• Experience with stream processing frameworks (Flink, Kafka Streams).