Data Engineer

• Design, build, and maintain data platforms and pipelines, covering ingestion, transformation, processing, and delivery of data.
• Develop and maintain ETL/ELT processes and orchestrations using tools such as Airflow, Azure Data Factory, or Databricks.
• Build data models aligned with the organization’s data architecture.
• Integrate data from multiple sources (batch and real-time: APIs, file transfer, data streaming).
• Apply data governance and data quality principles, including lineage tracking, access control, and quality checks.
• Write scripts and code to automate pipelines, validations, and governance tasks.
• Collaborate with data stewards, engineers, analysts, and business teams to deliver trusted, clean, and compliant data.
• Support data analytics and reporting through visualization tools such as Power BI and Tableau.
• Stay up-to-date with industry trends, best practices, and emerging technologies in Data Engineering.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Systems, or a related field.
• Minimum 2–3 years of experience as a Data Engineer or in a related role.
• Strong experience with SQL and NoSQL databases.
• Proficiency in Python for scripting and automating workflows.
• Knowledge of data modeling and data architecture
• Hands-on experience with ETL/Orchestration tools (e.g., Airflow, Azure Data Factory, Databricks).
• Knowledge of Data Governance & Data Quality frameworks and tools (DBT, Collibra, OpenMetadata, DataHub, etc.).
• • Familiarity with cloud data platforms (Azure, AWS, or GCP).
• Strong problem-solving, collaboration, and communication skills.
• Experience with big data frameworks (Spark, Hadoop)
• Hands-on experience with CI/CD pipelines and version control (Git, GitHub, GitLab)
• Knowledge of containerization and orchestration (Docker, Kubernetes) is a plus

Skills