Senior Data Engineer

About the Role

We are seeking a Senior Data Engineer to design, build, and scale modern data platforms that enable analytics, data products, and AI initiatives across the enterprise. In this role, you will develop robust data ingestion and transformation frameworks, optimize data pipelines, and deliver trusted, high-quality datasets that support business decision-making and innovation.

The ideal candidate has strong experience in cloud-based data engineering, data warehousing, and large-scale ETL/ELT development, along with expertise in Snowflake, Fivetran, dbt, AWS Glue, Python, and SQL.

Responsibilities

• Design, develop, and maintain scalable data pipelines and data models within Snowflake.
• Build and manage data ingestion frameworks using Fivetran and other cloud-native integration technologies.
• Develop and optimize ETL/ELT workflows using dbt, AWS Glue, Python, and PySpark.
• Architect and support data lake and data warehouse solutions on AWS and Snowflake.
• Perform source-to-target mapping and support enterprise data migration and modernization initiatives.
• Implement data quality, validation, reconciliation, and monitoring processes to ensure data reliability and accuracy.
• Develop reusable frameworks, automation capabilities, and standardized pipeline patterns to improve engineering efficiency.
• Partner with architects, analytics teams, and business stakeholders to deliver scalable and trusted data products.
• Prepare, transform, and manage datasets that support advanced analytics, machine learning, and generative AI use cases.
• Apply DataOps, CI/CD, governance, security, and operational best practices across the data ecosystem.
• Troubleshoot performance issues and continuously improve the scalability, reliability, and maintainability of data solutions.

Minimum Qualifications

• Bachelor's degree in Computer Science, Information Technology, Engineering, or a related technical field, or equivalent practical experience.
• 7 to 10 years of experience in Data Engineering, Data Integration, or related disciplines.
• Experience designing and developing enterprise-scale data pipelines and ETL/ELT solutions.
• Hands-on experience with:
  • Snowflake
  • Fivetran
  • dbt
  • AWS Glue
  • Python
  • SQL
  • PySpark
• Experience with data warehousing, dimensional modeling, and data lake architectures.
• Experience implementing data quality, monitoring, and validation frameworks.
• Strong understanding of software engineering best practices, version control, and CI/CD methodologies.

Preferred Qualifications

• Experience supporting machine learning, AI, or generative AI data pipelines.
• Experience with DataOps practices and automated data platform operations.
• Knowledge of infrastructure automation and cloud-native architecture patterns.
• Experience optimizing large-scale data processing workloads and query performance.
• Ability to influence technical direction and collaborate effectively across engineering, analytics, and business teams.
• Strong problem-solving, communication, and stakeholder management skills.

Technical Expertise

Data Engineering & Analytics

• Data Warehousing
• Data Lake Architecture
• ETL/ELT Development
• Source-to-Target Mapping
• Data Quality Frameworks
• Pipeline Automation
• Performance Optimization

Cloud & Data Platforms

• Snowflake
• Fivetran
• dbt
• AWS Glue
• Python
• SQL
• PySpark

DevOps & DataOps

• Git
• CI/CD Pipelines
• Agile Delivery
• Infrastructure Automation
• DataOps Best Practices

Apply now