Data Engineer (Nuc Job/ 1)

We are seeking a highly skilled Data Engineer to design, build, and maintain scalable data infrastructure and pipelines. The ideal candidate will be proficient in modern data engineering tools and cloud technologies, ensuring efficient data flow and availability across analytics and business intelligence systems.

Key Responsibilities

• Design, develop, and maintain ETL/ELT data pipelines from multiple sources to data warehouses or data lakes.

• Build and optimize data architectures that support analytics, reporting, and machine learning workloads.

• Collaborate with data scientists, analysts, and business teams to understand data requirements and ensure high data quality.

• Implement and manage data ingestion, transformation, and integration processes using modern tools and frameworks.

• Develop and maintain data models and schemas for structured and unstructured data.

• Monitor, troubleshoot, and improve data pipeline performance and reliability.

• Ensure data governance, security, and compliance standards are met.

• Work with cloud platforms such as AWS, Azure, or GCP for data storage and processing.

• Automate workflows and improve data operations using scripting and orchestration tools.

Required Skills and Qualifications

• Bachelor’s or Master’s degree in Computer Science, Information Technology, Data Engineering, or related field.

• 2–5 years of experience as a Data Engineer or similar role.

• Proficiency in SQL, Python, and data manipulation frameworks (Pandas, PySpark, etc.)

• Experience with ETL tools (e.g., Apache Airflow, Talend, AWS Glue, or Azure Data Factory).

• Strong understanding of data warehousing (e.g., Snowflake, Redshift, BigQuery).

• Familiarity with big data technologies (Hadoop, Spark, Kafka).

• Experience working with cloud data platforms (AWS S3, Azure Data Lake, GCP BigQuery).

• Solid grasp of data modeling, schema design, and data lifecycle management.

Preferred Qualifications

• Experience with CI/CD pipelines for data workflows.

• Knowledge of API data integration and real-time data streaming.

• Exposure to containerization tools (Docker, Kubernetes).

• Understanding of machine learning data preparation and feature engineering.