Lead Data Solutions Specialist
2 weeks ago
We are seeking a talented Data Engineer with strong Snowflake expertise to join our team. This role involves designing, building, and maintaining scalable and efficient data pipelines and solutions that support data-driven decision-making.
You will work closely with cross-functional teams including Data Scientists, Architects, and Software Engineers to create reliable, secure, and high-performing systems.
Required Skills & Qualifications- At least 1 year of production experience with Snowflake
- Strong understanding of data modeling (Data Vault), data warehousing (Redshift), and ETL/ELT tools (Apache Airflow, dbt)
- Solid programming skills in Python (at least 2 years for Middle, and 4 years for Senior)
- Strong knowledge of SQL and database optimization
- Experience in cloud platforms: AWS (S3, Lambda, Kinesis, Batch, DynamoDB, Athena, Glue, etc.)
- Ability to work with large volumes of data efficiently
- Familiarity with Agile methodologies and working in cross-functional teams
- Strong analytical thinking, problem-solving skills, and attention to detail are important
- Excellent communication and teamwork skills
- Upper-intermediate English or above
- Sport subscription
- Training budget
- Private healthcare
- Language classes
- Innovative projects
- Certificates
- Integration events
- Flat structure
- International projects
- Free coffee
- Bike parking
- Playroom
- In-house trainings
- Modern office
- No dress code
- Free beverages
- Design, develop, and optimize scalable data pipelines and ETL/ELT processes.
- Work with structured and unstructured data from various sources.
- Collaborate with Data Scientists, Software Engineers, and Business Analysts to meet data requirements.
- Implement data quality, data validation, and monitoring practices.
- Participate in architecture and design discussions to build cloud-native data platforms.
- Maintain and optimize data storage solutions (data lakes, data warehouses).
- Ensure the security, integrity, and availability of data.
- Support the deployment of machine learning models into production.
- Continuously improve performance, reliability, and scalability of our data systems.
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