data engineer

No description

• Design, develop, and maintain ETL/ELT pipelines for data ingestion, transformation, and loading;
• Build and optimize data processing workflows using Python and PySpark;
• Develop data warehouse architectures and data lake solutions;
• Create and maintain SQL queries, stored procedures, and data models for reporting;
• Integrate data from APIs, databases, streaming services, and third-party platforms;
• Implement data quality checks, validation rules, and monitoring mechanisms;
• Orchestrate workflows using Apache Airflow, Dagster, or similar tools;
• Collaborate with data scientists and analysts to deliver clean, reliable datasets;
• Document data architectures, pipeline logic, and data dictionaries.

• 3+ Years of commercial experience in data engineering or related roles;
• Strong proficiency in Python for data processing and automation;
• Expert-level SQL skills including complex queries, window functions, and query optimization;
• Hands-on experience with Apache Spark and PySpark for distributed data processing;
• Solid understanding of ETL/ELT concepts and data pipeline design patterns;
• Experience with relational databases, data warehouses, and data lakes;
• Experience with cloud platforms and their data services;
• Understanding of data modeling;
• Experience with workflow orchestration tools;
• Familiarity with streaming data processing;
• Knowledge of Git, data governance, security, and compliance principles;
• Strong analytical and problem-solving skills with attention to data accuracy;
• English: B2 or higher (written and spoken);
• Nice to have: Experience with infrastructure-as-code (Terraform), dbt, CI/CD, NoSQL databases, BI tools, real-time analytics frameworks, MLOps concepts, machine learning pipelines, and contributions to open-source data engineering projects.

• No conditions specified