Design Data Warehouse/Data Lake/Data Lakehouse architectures to meet analytics and big data processing requirements while maintaining data infrastructure.
Build, optimize, and monitor ETL/ELT pipelines (batch and streaming) to collect and process data from multiple sources.
Integrate data from various sources (DB, API, Cloud services, IoT, etc.).
Implement high-performance data storage solutions (Snowflake, BigQuery, Redshift, Hadoop, Spark).
Ensure data security, integrity, and compliance with regulations (GDPR, ISO 27001, etc.).
Optimize query performance while maintaining data integrity and security.
Support
Data Analysts and
Data Scientists by providing clean, complete, and timely datasets.
Propose improvements to data infrastructure for cost efficiency and performance optimization.
Bachelor's degree in IT, Data Science, or related fields.
Minimum 5 years of experience in Data Engineering or Backend development with a focus on data processing.
Advanced proficiency in SQL and at least one programming language for data processing (Python, Scala, or Java).
Hands-on experience with ETL/ELT tools (Airflow, Talend, dbt, etc.).
Experience with distributed data processing systems (Spark, Flink, Kafka).
Experience working with real-time data (Kafka, Spark Streaming).
Experience deploying data pipelines on cloud platforms (AWS/GCP/Azure).
Strong understanding of data modeling (star schema, snowflake schema, data vault).
Familiarity with data storage systems such as Snowflake, BigQuery, Redshift, or the Hadoop ecosystem.