Data Engineer

Muller's Solutions is seeking an experienced data engineer with 5–6 years of experience in designing, developing, and maintaining scalable data solutions. The ideal candidate should possess strong expertise in Python, Pandas, NumPy, Airflow, BigQuery, SQL, and API development.

The candidate will play a key role in building robust data pipelines, processing large datasets, and supporting business-critical applications related to inventory optimization, master data management, and backend services.

Key responsibilities

• Design, develop, and maintain scalable ETL/ELT pipelines for data ingestion and processing.
• Build and optimize data workflows using Apache Airflow.
• Develop and maintain data processing solutions using Python, Pandas, and NumPy.
• Design and optimize complex SQL queries and data models in BigQuery.
• Develop and integrate APIs to support data exchange between systems.
• Implement data ingestion frameworks from various internal and external sources.
• Perform data cleansing, transformation, and validation to ensure data accuracy and consistency.
• Develop and maintain processes for master data management.
• Support inventory optimization initiatives by implementing data processing and business logic.
• Build and maintain backend services that enable data-driven applications.
• Monitor and troubleshoot data pipelines to ensure reliability and performance.
• Collaborate with cross-functional teams, including product, analytics, and engineering teams.
• Ensure adherence to data governance, security, and best practices.

Requirements

Technical requirements

Must-have skills

• 5–6 years of experience in data engineering or related roles.
• Strong hands-on experience with: Python, Pandas, NumPy, Apache Airflow, Google BigQuery, SQL, API development and integration.
• Experience in designing and implementing scalable ETL/ELT pipelines.
• Strong understanding of data modeling, data transformation, and database concepts.
• Experience working with large-scale datasets and optimizing query performance.
• Proficiency with version control systems such as Git.

Good-to-have skills

• Experience with PySpark and distributed data processing.
• Basic understanding of machine learning concepts and workflows.
• Familiarity with data quality frameworks and data validation practices.
• Experience with Docker and containerization technologies.
• Knowledge of CI/CD pipelines and DevOps practices.
• Experience working in cloud-based data environments.

Preferred qualifications

• Bachelor's degree in computer science, information technology, data science, or a related field.
• Strong analytical and problem-solving skills.
• Excellent communication and collaboration abilities.
• Experience working on enterprise-scale data platforms and data-driven applications.