Data Engineer

• Bachelor’s degree in Information Technology, Information Systems, or a related field; Master’s degree is a plus.
• Experience in data engineering, data transformation, or data integration roles is a plus. Fresh Grad is welcome.
• Proven experience designing, developing, and maintaining ETL/ELT data pipelines for structured and semi-structured data using tools such as dbt, Informatica, Talend, or Apache Airflow.
• Hands-on experience working with cloud-based data platforms such as Snowflake, Databricks, Google BigQuery, Amazon Redshift, or Azure Synapse.
• Strong proficiency in SQL and practical experience with at least one programming language (e.g., Python, Scala, or Java) for automation and data manipulation.
• Demonstrated experience integrating and preparing data for Business Intelligence (BI) tools, such as Power BI, Qlik Sense, or Tableau, ensuring optimized data structures for visualization and analysis.
• Experience collaborating closely with business analysts, functional teams, and data consumers to translate business requirements into technical data solutions.
• Exposure to version control systems (Git) and CI/CD workflows, ideally within an Agile development environment.

Technical skills:

• SQL, Python, or Scala.
• TL/ELT tools (e.g., dbt, Informatica, Talend).
• Cloud data platforms (e.g., Snowflake, Databricks, BigQuery, Redshift).
• CI/CD and version control (e.g., Git, Jenkins)

Soft skills:

• Analytical Thinking Ability to interpret complex data processes, identify root causes of issues, and propose practical solutions.
• Business Acumen Understands business processes and can translate functional requirements into technical data solutions that add measurable value.
• Communication Skills Able to communicate clearly with both technical and non-technical stakeholders, including data architects, analysts, and business users.
• Collaboration & Teamwork Works effectively within cross-functional teams, fostering cooperation between IT, data, and business units.
• Problem-Solving Mindset Approaches challenges systematically, balancing technical precision with business needs.
• Attention to Detail Ensures data accuracy and consistency throughout transformation and integration processes.
• Continuous Learning Stays updated with emerging data technologies, engineering frameworks, and best practices.