Data Engineer

• Data Pipeline Development:
• Design, build, and maintain scalable data pipelines and ETL processes to support analytics, reporting, and data science initiatives.
• Productionization & Automation:
• Develop and automate robust workflows for data ingestion, transformation, and delivery using orchestration tools and CI/CD pipelines.
• Cloud Data Engineering:
• Leverage cloud platforms (Azure preferred) and technologies (Databricks, Azure Data Factory) to manage big data environments and enable advanced analytics.
• Code Quality & Collaboration:
• Participate in code reviews (PRs), enforce coding standards, and collaborate with cross-functional teams to ensure maintainable, high-quality solutions.
• Monitoring & Troubleshooting:
• Implement monitoring and observability for data pipelines, proactively identifying and resolving issues in production environments.
• Data Governance & Quality:
• Ensure data integrity, security, and compliance by applying best practices in data governance, quality management, and documentation.
• Stakeholder Engagement:
• Work closely with business partners to understand requirements, deliver custom data solutions, and provide training and support for end users.

What You'll Need

Education:

• Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field (Master's preferred).

Experience:

• 2-3 years of relevant experience, or a Master's degree in a related field.
• Strong proficiency with SQL, Python, and PySpark for data engineering tasks.
• Hands-on experience with cloud computing technologies for big data (Azure preferred), including Databricks and Azure Data Factory.
• Experience building and maintaining production-grade data pipelines, including monitoring and troubleshooting in live environments.
• Proficiency in using Git for version control and collaboration; experience participating in code reviews (PRs) and enforcing code quality standards.
• Experience working in an Agile environment, with knowledge of Agile methodologies and practices.
• Hands-on experience with CI/CD pipelines for automating data pipeline deployment and testing.

Technical Skills:

• Experience with orchestration tools (e.g., Airflow, dbt) and containerization (Docker, Kubernetes) for scalable data operations.
• Familiarity with automation tools and processes for streamlining development, testing, and deployment workflows.
• Understanding of DataOps concepts and best practices for reliable, reproducible, and scalable data workflows.
• Ability to develop dashboards and data visualizations in Power BI or similar tools.
• Basic understanding of data science concepts, techniques, and tools, such as machine learning algorithms, statistical analysis, and data preprocessing.

Business & Communication Skills:

• Exceptional communication skills, with the ability to translate technical solutions into business value.
• Ability to take vague requirements and convert them into scalable, actionable data solutions.
• Strong problem-solving skills, business curiosity, and results-driven mindset.
• Commitment to continuous learning and staying at the forefront of technology trends.
• Takes initiative and builds relationships with key stakeholders to establish confidence, trust, and credibility.
• Customer-focused and motivated by results, with the ability to proactively identify business needs and drive stakeholder satisfaction.

Preferred Qualifications

• Deep understanding and experience with Azure's cloud architecture, services, and management tools.
• Knowledge of MLOps best practices, including deployment, monitoring, and maintenance of machine learning models.
• Experience participating in code reviews and collaborative development processes.
• Experience with building automated pipelines for data workflow deployment and monitoring.
• Experience supporting data-driven business transformation and data governance initiatives