Data Engineer

Overview

• Design and implement scalable, secure, and efficient cloud-based data solutions for enterprise systems.
• Develop and optimize ETL/ELT pipelines to integrate diverse internal and external data sources.
• Lead modernization initiatives to enhance data reliability and operational efficiency across platforms.
• Collaborate with cross-functional teams to translate complex requirements into scalable data architectures.
• Establish and enforce governance, security, and quality standards for data ecosystems.
• Enable advanced analytics and reporting through BI tools and semantic data models.
• Evaluate and integrate emerging data technologies to drive innovation and scalability.
• Mentor team members and lead workshops to advance data engineering practices.
• Travel up to 15% to support collaboration and business needs.

Key Responsibilities & Duties

• Design, build, and maintain scalable cloud-based data solutions using modern tools and services.
• Develop and optimize ETL/ELT pipelines for high-volume enterprise data environments.
• Lead initiatives to modernize data platforms and reduce manual processes.
• Define and enforce architecture, governance, and security standards for data systems.
• Collaborate with BI teams to enable self-service analytics and trusted insights.
• Monitor and optimize the performance, availability, and cost efficiency of data platforms.
• Translate complex business requirements into scalable and efficient data architectures.
• Support training initiatives and mentor team members in data engineering practices.
• Adopt and integrate new data technologies to enhance platform capabilities.

Job Requirements

• Bachelor’s degree in Computer Science, Engineering, Information Systems, or equivalent practical experience.
• 5+ years of experience in data engineering, with 3+ years in cloud-based solutions.
• Proficiency in modern cloud data tools like Azure, Databricks, or Snowflake.
• Proven expertise in building and optimizing large-scale ETL/ELT pipelines.
• Advanced SQL skills and experience with data modeling for analytics workloads.
• Knowledge of governance, security, and quality standards in cloud platforms.
• Preferred certifications in Azure, AWS, Snowflake, or Databricks.
• Experience with BI tools like Power BI and enabling self-service analytics.
• Strong communication skills for collaboration across teams and stakeholders.