Second Talent Hiring! Full Time Data Engineer in Federal Territory - Ricebowl

Job Description:

• Plan, implement and maintain the company’s data platform and solutions.
• Design, build and automate modern data pipelines and data streams.
• Develop and maintain data models and schemas that facilitate data integra on and analysis.
• Implement effective metrics, monitor, and proactively optimize ETL/ELT process performances, troubleshoot issues, and implement improvements to enhance data process efficiency in thedata lifecycle.
• Collaborate with data analysts, data stewards and business stakeholders to understand their data requirements and translate them into technical specifications.
• Ensure data quality and accuracy by performing data profiling, data cleansing, and data validation.
• Evaluate the performance and applicability of data tools against user requirements.
• Perform related du es as assigned, within the scope of prac ce (e.g., data quality and data education programs).

Requirements:

• Degree holder in Computer Science, Information Technology, or relevant disciplines.
• At least three years of experience in data engineering.
• Proficiency level of SQL is a must.
• Hands on experience in Azure Databricks or Synapse.
• Hands on experience with Microso Fabric
• Hands on experience in programming languages for data engineering (e.g., PySpark, Python, R, Java, Scala).
• Experience in designing and deploying data applications on cloud solutions, such as Azure.
• Possess excellent problem-solving skills and data-driven thinking capabilities.
• Ability to work independently and as a member of a team, self-motivated.
• Good communication in both written and spoken English.
• Willingness to travel, as needed.

The requirements below are considered as advantages, but not required:

• Certified Azure Data Engineer Associate.
• Knowledge of Azure Data Factory, Azure Data Lake, Azure Event Hub, or Power BI.
• Performance tuning and op mizing code running in Azure Databricks or Synapse environment.
• Experience in preparing data for data science and machine learning.
• Experience in DataOps.
• Experience in working in a global team.