Responsibilities:

Guide and plan data engineering processes for digital systems and products, providing technical leadership and shaping strategy, architecture, and implementation aligned with national digital government standards.
Lead technical discussions with data engineering teams and stakeholders, establishing data architecture standards, DevOps best practices, and ensuring consistency, quality, and system reliability across initiatives.
Design, develop, and maintain data pipelines, databases, and data lakes to collect, process, harmonize, and store data from multiple platforms, supporting analytical, reporting, and AI workloads.
Implement data validation, cleansing, and monitoring processes to ensure data quality, integrity, and compliance with governance and security requirements.
Collaborate with product teams, analysts, and stakeholders to translate business requirements into technical specifications, scalable solutions, and data visualizations for reporting.
Mentor and develop the data engineering team’s capabilities, promote knowledge sharing, and contribute to process improvements and adoption of emerging technologies.
Maintain documentation of data systems, configurations, and engineering processes, and troubleshoot issues to ensure reliable system performance.

Requirements

Familiarity with big data frameworks (e.g., Spark), Databricks, version control systems (e.g., Git), and data governance, security, and compliance best practices.
Bachelor’s degree in Computer Science, Data Science, Engineering, Information Technology, or a related technical field.
5+ years of experience in data engineering, cloud infrastructure, platform engineering, or similar technical roles, including experience with cloud platforms such as AWS, Azure, or GCP.
Strong programming skills in Python, Java, or Scala, and solid knowledge of SQL, relational and non-relational databases, data modelling, and ETL/ELT processes.
Experience designing, developing, and maintaining data pipelines, data lakes, and databases to support analytics, reporting, and AI workloads.
Experience with business intelligence and data visualization tools such as Power BI or AWS QuickSight.
Certifications such as AWS Certified Data Engineer or Databricks Certified Data Engineer will be good to have.

Data Engineer, Big Data (Databricks)