Data Engineer

Responsibilities

• Profile, analyze, and validate source data to understand structure, quality, business meaning, and transformation requirements.
• Design, develop, test, and maintain data pipelines that ingest, transform, and deliver data from internal and external sources.
• Build scalable ETL/ELT solutions across cloud data platforms, data warehouses, lakehouse patterns, and data integration tools.
• Develop and optimize SQL, stored procedures, scripts, and reusable data engineering components.
• Implement data quality checks, reconciliation controls, error handling, logging, lineage capture, and pipeline monitoring.
• Apply automated testing, version control, code review, continuous integration, and deployment practices.
• Monitor pipeline execution, investigate failures, resolve data issues, and support root cause analysis.
• Collaborate with business users, data owners, data stewards, architects, security teams, and platform engineers.
• Document technical designs, mappings, transformation logic, operational procedures, dependencies, and support information.
• Read and follow the Underwriters Laboratories Code of Conduct and all physical and digital security practices.

Qualifications

Education & Experience

• Bachelor's degree in computer science, Information Systems, Engineering, Data Analytics, Mathematics, or a related discipline, or equivalent combination of education and experience.
• 3+ years of experience designing, developing, testing, and supporting data pipelines, ETL/ELT processes, integrations, or analytics data solutions, preferably within a modern cloud-based data lakehouse environment.

Technical Skills

• Strong SQL expertise, including query development, transformation logic, troubleshooting, optimization, and performance tuning.
• Proficiency in Python development for data engineering and analytics solutions.
• Experience building and maintaining scalable ETL/ELT data pipelines on modern data platforms.
• Hands-on experience with cloud data technologies and platforms such as Microsoft Azure, Snowflake, SQL Server, or similar solutions.
• Solid understanding of dimensional data modeling methodologies (Kimball), curated datasets, semantic layers, and analytics-ready data products.
• Knowledge of data profiling, data quality, validation, and management of structured, semi-structured, and unstructured data.
• Experience utilizing Git-based version control, code reviews, automated testing, CI/CD pipelines, and Agile delivery practices.
• Understanding of secure data handling, governance, lineage, privacy, and access control principles.

Professional Skills

• Strong analytical thinking, problem-solving, and root-cause analysis capabilities.
• Ability to collaborate effectively with both business stakeholders and technical teams.
• Excellent communication skills, with the ability to translate technical concepts into business value.
• Inquisitive mindset, strong attention to detail, and commitment to delivering high-quality solutions.

Continuous learning mindset with a willingness to adopt new technologies, tools, and best practices.