Data Engineer

Job Requirements

At Quest Global, it’s not just what we do but how and why we do it that makes us different. With over 25 years as an engineering services provider, we believe in the power of doing things differently to make the impossible possible. Our people are driven by the desire to make the world a better place—to make a positive difference that contributes to a brighter future. We bring together technologies and industries, alongside the contributions of diverse individuals who are empowered by an intentional workplace culture, to solve problems better and faster

We are known for our extraordinary people who make the impossible possible every day. Questians are driven by hunger, humility, and aspiration. We believe that our company culture is the key to our ability to make a true difference in every industry we reach. Our teams regularly invest time and dedicated effort into internal culture work, ensuring that all voices are heard.

Job Summary
We are looking for an experienced Data Engineer with strong expertise in Python, SQL,
ETL/ELT development, and cloud-based data platforms. The ideal candidate will be
responsible for designing, developing, and maintaining scalable data pipelines, data
warehouses, and analytics-ready datasets that support reporting, business intelligence,
and data-driven decision-making.
The role requires hands-on experience working with large datasets, modern data
engineering frameworks, cloud technologies, and data orchestration tools while
collaborating closely with Data Analysts, Power BI Developers, Product Teams, and
Business Stakeholders.

Key Responsibilities
Data Engineering & Development
• Design, develop, and maintain scalable data pipelines using Python and SQL.
• Build robust ETL/ELT frameworks for structured and unstructured data processing.
• Develop data ingestion solutions from APIs, databases, cloud storage, and external
file sources.
• Perform data cleansing, transformation, enrichment, validation, and quality
assurance activities.
• Design reusable and maintainable data processing components.
Cloud & Big Data Platforms
• Develop and support cloud-native data solutions on AWS and/or Azure.
• Build and optimize data workflows using services such as AWS Glue, EMR, Lambda,
S3, RDS or Azure Data Factory, Databricks, Synapse, and ADLS.
• Work with large-scale datasets and distributed data processing frameworks.
Data Warehouse & Analytics
• Design and maintain data models, data marts, and analytical datasets.
• Support enterprise reporting and self-service analytics initiatives.
• Optimize database performance and query execution.
• Collaborate with Power BI teams on semantic models and reporting datasets.
Data Quality & Operations
• Implement data validation frameworks and quality controls.
• Develop monitoring, alerting, logging, and operational support processes.
• Troubleshoot production data pipeline failures and performance issues.
• Support ongoing maintenance and improvements of enterprise data platforms.
Engineering Excellence
• Follow coding standards, peer reviews, and software engineering best practices.
• Implement CI/CD pipelines for data engineering workloads.
• Maintain source control repositories using Git.
• Create technical documentation and knowledge-sharing materials.

We wholeheartedly believe in the diversity of thought that comes with fostering a culture rooted in respect, where everyone belongs, is valued, and feels inspired to share their ideas. We know embracing our unique differences makes us better, and that solving the worlds hardest engineering problems requires diverse ideas, perspectives, and backgrounds. We shine the brightest when we tap into the many dimensions that thrive across over 21,000 difference-makers in our workplace.

Work Experience

Programming
• Python (Advanced)
• SQL (Advanced)
• Linux/Unix Shell Scripting
Data Engineering
• ETL/ELT Development
• Data Pipeline Development
• Data Ingestion Frameworks
• Data Transformation
• Data Validation
• Data Quality Management
Big Data & Processing
• Apache Spark
• PySpark
Workflow Orchestration
• Apache Airflow
• Workflow Scheduling Tools
Databases & Warehousing
• PostgreSQL
• SQL Server
• Snowflake
• Amazon Redshift
• Data Warehousing Concepts
• Data Modeling
DevOps & Version Control
• Git
• CI/CD Fundamentals
Integration
• REST API Integration