Senior Data Engineer with AWS

Requirements

• Experience in data engineering;
• Experience working with Cloud Solutions (preferably AWS, also GCP or Azure);
• Experience with Cloud Data Platforms (e.g., Snowflake, Databricks);
• Proficiency with Infrastructure as Code (IaC) technologies like Terraform or AWS CloudFormation;
• Experience handling real-time and batch data flow and data warehousing with tools and technologies like Airflow, Dagster, Kafka, Apache Druid, Spark, dbt, etc.;
• Proficiency in programming languages relevant to data engineering such as Python and SQL;
• Experience in building scalable APIs;
• Experience in building Generative AI Applications (e.g., chatbots, RAG systems);
• Familiarity with Data Governance aspects like Quality, Discovery, Lineage, Security, Business Glossary, Modeling, Master Data, and Cost Optimization;
• Advanced or Fluent English skills;
• Strong problem-solving skills and the ability to work collaboratively in a fast-paced environment.

Nice to Have:

• Relevant AWS, GCP, Azure, Databricks certifications;
• Knowledge of BI Tools (Power BI, QuickSight, Looker, Tableau, etc.);
• Experience in building Data Solutions in a Data Mesh architecture;
• Familiarity with classical Machine Learning tasks and tools (e.g., OCR, AWS SageMaker, MLFlow, etc.).

Responsibilities:

• Collaborate closely with clients to deeply understand their existing IT environments, applications, business requirements, and digital transformation goals;
• Collect and manage large volumes of varied data sets;
• Work directly with Data Scientists and ML Engineers to create robust and resilient data pipelines that feed Data Products;
• Define data models that integrate disparate data across the organization;
• Design, implement, and maintain ETL/ELT data pipelines;
• Perform data transformations using tools such as Spark, Trino, and AWS Athena to handle large volumes of data efficiently;
• Develop, continuously test and deploy Data API Products with Python and frameworks like Flask or FastAPI.