- Work model
- Hybrid
- Experience
- 5+ years
- Employment
- Full Time
- Compensation
- Not disclosed
- Technology signal
- 10 tags
Technology context
10Parsed from the vacancy text; ordered by relevance to this role.
AIAWSPythonKafkaSparkSnowflakeDatabricksNoSQLSQLAirflow
Full listing
Role description
We are looking for a strong, hands-on Data Engineer to join our team and play a key role in building our data infrastructure from the ground up. In this role, you will design and implement scalable data pipelines and platforms, supporting both batch and real-time use cases. You will work closely with analysts and stakeholders to deliver reliable, high-quality data solutions, and take full ownership of data flows - from ingestion to consumption. This is a great opportunity for an executor who enjoys building, moving fast, and making an impact.
What will your job look like?
- Design, build, and maintain robust and scalable data pipelines (batch and real-time) end-to-end.
- Design and implement scalable, flexible data architectures to support evolving business needs.
- Build and manage data platforms, including data lakes and data warehouses.
- Integrate multiple data sources (structured and unstructured) into a unified data platform using batch (ETL) and real-time streaming solutions.
- Design and implement efficient data models, schemas, and database structures (SQL / NoSQL).
- Develop and implement data quality processes to ensure accuracy, consistency, and reliability.
- Monitor, optimize, and troubleshoot data infrastructure to meet performance and SLA requirements.
All you need is
- 5+ years of hands-on experience as a Data Engineer, building data systems from scratch in dynamic environments.
- Bachelor's degree in Computer Science, Engineering, or a related field (or equivalent practical experience).
- Strong proficiency in Python and advanced SQL , with solid experience in data modeling.
- Proven experience designing and building scalable data pipelines ( batch and real-time ), including streaming technologies such as Kafka.
- Strong experience working with AWS , including services such as S3 , Athena and DynamoDB.
- Experience working with big data processing frameworks such as Spark , and columnar data formats (e.g., Parquet ).
- Hands-on experience with workflow orchestration tools such as Airflow.
- Strong ownership and execution mindset, with excellent problem-solving skills and high attention to detail, and the ability to collaborate effectively and deliver in ambiguous, fast-paced environments.
- Experience with data platform technologies such as Databricks , Snowflake - Advantage.
- Experience building data platforms using modern lakehouse technologies (e.g., Iceberg ) - Advantage.
- Fluent in English.