Join a dynamic team as a Data Engineer, where you'll play a critical role in designing, developing, and maintaining our data infrastructure. In this position, you will be responsible for building robust data pipelines and integrating data from diverse sources to support our analytics and business intelligence efforts. If you're passionate about data, enjoy solving complex problems, and thrive in a collaborative environment, we invite you to apply and help us advance our data capabilities.
Responsibilities:
- Develop Data Pipelines : Create and maintain ETL pipelines to move data into centralized storage, ensuring data quality and integrity.
- Integrate Data : Combine data from various sources, including databases, APIs, and external providers, to build a unified data foundation.
- Data Transformation : Clean, normalize, and aggregate data for analysis, reporting, or machine learning.
- Best Practices & Frameworks : Develop and implement frameworks for data pipeline development, deployment, and automation.
- Data Governance : Enforce data governance standards.
- Collaborate with Teams : Work with analytics, product, and infrastructure teams to advance data and analytics platforms.
- Monitor Systems : Ensure the reliability of data systems through monitoring, issue detection, and automated error handling.
Requirements:
- Experience in designing data solutions and data modeling.
- Strong skills in developing data processing jobs using PySpark or SQL.
- Proficiency in data pipeline orchestration tools like ADF or Airflow.
- Experience with real-time and batch data processing.
- Familiarity with building data pipelines on Azure.
- Proficient in SQL and experienced with advanced features like Window functions.
- Understanding of DevOps, CI/CD pipelines, and Git workflows.
- Capable of working with both business and technical stakeholders.
Preferred Qualifications:
- Bachelor’s degree in a relevant field.
- 3-5 years of data engineering experience.
- Experience with agile/scrum methodologies.
- Knowledge of Azure Data Factory, Databricks, Snowflake, and shell scripting.
- Proficiency in Python, especially for data-related tasks.
- Familiarity with Apache Spark, Kafka, Kubernetes, Docker, and cloud infrastructure management (e.g., Terraform).
- Experience in ML/AI model development and deployment is a plus.
