Data Engineer (AWS) AND Data Modeler - Hybrid

We are looking for a skilled and proactive Data Engineer to join our growing data platform team. You will design, build, and optimize scalable data pipelines and cloud-native data solutions that support analytics, reporting, and business intelligence initiatives across the organization.

• Design, develop, and maintain scalable ETL/ELT pipelines
• Build and optimize data workflows using orchestration tools and cloud-native services
• Develop robust data models and transformations using DBT
• Manage and optimize data warehouse platforms such as Redshift and Snowflake
• Implement monitoring, automation, and performance tuning for data pipelines
• Collaborate with analysts, product teams, and stakeholders to understand data requirements
• Ensure data quality, governance, reliability, and security best practices
• Support CI/CD processes for data engineering deployments

Troubleshoot production issues and improve operational efficiency

Qualifications:

• Tech stack: AWS, Kubernetes, ETL, DBT (Data Build Tool), Argo Workflow, Redshift, Snowflake, Python, Shell scripting
• CI/CD – GitHub actions / Bamboo – existing templates

• Design and build dimensional data models in accordance to industry standards and best practices
• Develop conceptual, logical, and physical data models to organize and structure data efficiently
• Create and design the architecture of data marts (and data lakes, if we decide to adapt) to ensure efficient storage, retrieval, and analysis
• Design and help build complex queries on large volumes of data
• Ensure the data architecture is scalable, efficient, and optimized for performance, especially in large data environments.
• Work closely with stakeholders to gather and understand requirements and then translate business requirements into data solutions and specifications
• Collaborate with delivery teams (data engineering, reports and dashboard developers) to ensure data is accurately processed and loaded
• Standardize different queries and optimize them to speed up query time
• Ensure data models support fast query execution, even with complex joins or large datasets
• Perform periodic analysis of query and reports performance, and recommend optimization solutions
• Identify automation opportunities and implement automation projects
• Constant alignment with Enterprise Data Architect to ensure uniformity and consistency of design and solution
• Ensure compliance with data management, access, and compliance (e.g., GDPR, HIPAA) policies
• Create and maintain comprehensive design and solutions documentation (entity relationship diagram, mappings, data dictionary, etc.)
• Communicate complex data concepts to non-technical stakeholders to ensure understanding of data systems and solutions