Data Engineer

We are looking for a skilled Data Engineer to design, build, and maintain scalable and reliable data infrastructure that supports analytics, reporting, and data-driven decision-making across the organization. This individual contributor role has a direct impact on how data is collected, processed, secured, and made available for business and intelligence initiatives. The position works closely with technical and business teams to ensure data systems are robust, efficient, and aligned with organizational needs.

Key Responsibilities

• Design, build, and maintain scalable ETL and ELT data pipelines for both structured and unstructured data
• Develop and manage backup and archiving solutions to ensure data continuity and availability
• Consolidate, optimize, and manage data storage for efficient access and performance
• Monitor data systems and pipelines, implementing proactive error handling, logging, and performance tuning
• Implement and maintain data security, access controls, and privacy standards
• Test, validate, and document data pipelines to ensure reliability, scalability, and compliance
• Support the development of prototypes and proof-of-concept solutions for data-driven use cases
• Collaborate with cross-functional teams to integrate and operationalize business-critical data systems
  
  

Qualifications

The ideal candidate holds a bachelor’s degree in Computer Engineering, Computer Science, Information Systems, or a related field, and brings a strong technical foundation in data engineering and data platform development. The role requires hands-on expertise in designing and managing ETL processes using modern data integration tools such as Apache NiFi, Talend, or equivalent platforms.

Strong programming skills in Python and SQL are essential, along with practical experience working with big data technologies such as Hadoop, Spark, or Kafka. The candidate is comfortable working with both relational and NoSQL databases, including systems such as MySQL, PostgreSQL, and MongoDB.

Experience with cloud-based data platforms and warehouses—such as AWS, Azure, GCP, Google BigQuery, Amazon Redshift, or Snowflake—is important, along with an understanding of data security, governance, and access management practices. Familiarity with business intelligence tools such as Tableau or Power BI is expected, and exposure to machine learning or AI-enabled data applications is considered an advantage.

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