Data Engineer

About the role

We are seeking a Data Engineer to design, build, and optimize data pipelines and architectures that empower data-driven decision-making and business growth. In this hands-on technical role, you will collaborate with cross-functional teams to gather requirements, architect scalable data solutions, and implement best-in-class data engineering practices. You will play a key role in advancing our data analytics capabilities, ensuring data is reliable, accessible, and aligned with business objectives.

Responsibilities

• Design, develop, and maintain scalable data pipelines and architectures to support modern data analytics and integration needs.
• Build and optimize data models, including advanced dimensional modeling and semantic layer design, to enable efficient querying and reporting.
• Implement robust ETL/ELT processes using intermediate Python and advanced SQL to ensure high-quality data delivery.
• Collaborate with business units, analysts, and stakeholders to gather requirements and translate them into technical solutions that drive measurable business value.
• Ensure best-in-class implementation of data engineering solutions, focusing on performance, scalability, and maintainability.
• Contribute to the development and maintenance of data warehouses, data lakes, and integration pipelines.
• Proactively identify and resolve data quality issues, ensuring data integrity and consistency across systems.
• Work closely with data analysts and other engineers to support data-driven initiatives and deliver actionable insights.
• Stay current with industry trends and emerging tools to continuously improve data engineering practices.
• Document technical designs, processes, and solutions to support team collaboration and knowledge sharing.

Requirements

• Bachelor’s degree in Computer Science, Information Systems, Engineering, or equivalent professional experience in data engineering or related technical roles.
• 7+ years of experience in data engineering and analytics or data integration.
• Expertise in modern data analytics, including building and optimizing data pipelines for near-real-time and batch processing.
• Proven skills in data architecture, with a focus on designing scalable and efficient data systems.
• Advanced proficiency in SQL for complex querying, optimization, and data transformation.
• Intermediate proficiency in Python for scripting, automation, and ETL development.
• Advanced dimensional modeling skills, with experience designing star schemas and semantic layers for analytics.
• Expertise in building metadata driven pipelines, integrations, and methods for improving maintainability and reducing technical debt.
• Experience in semantic layer design to support business intelligence tools and self-service analytics.
• Strong collaboration skills, with the ability to work effectively with cross-functional teams and stakeholders.
• Able to gather requirements and translate them into technical solutions that align with business goals.
• Self-driven and motivated, with a track record of delivering high-impact results and moving the needle for business value.
• Experience with modern data platforms (e.g., DOMO, Databricks, Snowflake, Fabric) and tools.
• Knowledge of data governance, data quality, and data integration best practices is a plus.
• Professional certifications (e.g., Azure Data Engineer, AWS Certified Data Engineer, Google Cloud Professional Data Engineer) are a plus.

Qualifications

• Bachelor’s degree in Computer Science, Information Systems, Engineering, or equivalent professional experience in data engineering or related technical roles.
• 7+ years of experience in data engineering and analytics or data integration.
• Expertise in modern data analytics, including building and optimizing data pipelines for near-real-time and batch processing.
• Proven skills in data architecture, with a focus on designing scalable and efficient data systems.
• Advanced proficiency in SQL for complex querying, optimization, and data transformation.
• Intermediate proficiency in Python for scripting, automation, and ETL development.
• Advanced dimensional modeling skills, with experience designing star schemas and semantic layers for analytics.
• Expertise in building metadata driven pipelines, integrations, and methods for improving maintainability and reducing technical debt.
• Experience in semantic layer design to support business intelligence tools and self-service analytics.
• Strong collaboration skills, with the ability to work effectively with cross-functional teams and stakeholders.
• Able to gather requirements and translate them into technical solutions that align with business goals.
• Self-driven and motivated, with a track record of delivering high-impact results and moving the needle for business value.
• Experience with modern data platforms (e.g., DOMO, Databricks, Snowflake, Fabric) and tools.
• Knowledge of data governance, data quality, and data integration best practices is a plus.
• Professional certifications (e.g., Azure Data Engineer, AWS Certified Data Engineer, Google Cloud Professional Data Engineer) are a plus.

Skills

• Expertise in modern data analytics, including building and optimizing data pipelines for near-real-time and batch processing.
• Proven skills in data architecture, with a focus on designing scalable and efficient data systems.
• Advanced proficiency in SQL for complex querying, optimization, and data transformation.
• Intermediate proficiency in Python for scripting, automation, and ETL development.
• Advanced dimensional modeling skills, with experience designing star schemas and semantic layers for analytics.
• Expertise in building metadata driven pipelines, integrations, and methods for improving maintainability and reducing technical debt.
• Experience in semantic layer design to support business intelligence tools and self-service analytics.
• Strong collaboration skills, with the ability to work effectively with cross-functional teams and stakeholders.
• Able to gather requirements and translate them into technical solutions that align with business goals.
• Self-driven and motivated, with a track record of delivering high-impact results and moving the needle for business value.
• Experience with modern data platforms (eomo, databricks, snowflake, fabric).
• Knowledge of data governance, data quality, and data integration best practices is a plus.
• Professional certifications (e.g., azure data engineer, aws certified data engineer, google cloud professional data engineer) are a plus.

Benefits

• Medical, dental, and vision coverage.
• A 401(k) retirement plan.
• Paid Time Off.
• Health Savings and Flexible Spending Accounts (HSA/FSA).
• Life and disability insurance.
• Access to an Employee Assistance Program (EAP) to support your overall well-being.

Pay

TBD

Schedule

Hybrid or onsite work environment preferred, with remote consideration based on candidate qualifications and company policy. Occasional travel may be required for meetings and conferences.