Data Engineer

Role Summary

• Design, build, and maintain scalable data solutions that support reporting, self-service analytics, and advanced analytics.
• Work primarily on the organization’s data platform, currently centered on Snowflake and IICS, to transform raw data into trusted, business-ready data assets.
• Partner closely with Data Analysts and source-system teams to translate business needs into data models, pipelines, and reusable data products.
• Use AI-assisted development practices across the full delivery lifecycle to improve design quality, development efficiency, testing, documentation, and technical communication.

• Apply modern data engineering practices while maintaining strong fundamentals in data modeling, ETL/ELT, orchestration, and data quality.

   

Responsibilities

• Design, develop, and maintain data pipelines, transformations, and orchestration workflows that ingest, curate, and publish data for analytics use cases.
• Build and enhance data models based on business analysis and reporting requirements.
• Work with structured and semi-structured data from enterprise systems and files, including relational databases, JSON, and XML.
• Collaborate with Data Analysts, source-system developers, and business stakeholders to define requirements and deliver fit-for-purpose data solutions.
• Improve engineering productivity by using AI tools for code support, testing framework creation, documentation, diagramming, and solution exploration.
• Support data quality, performance optimization, maintainability, and operational reliability of data solutions.
• Enable business reporting, self-service analytics, and foundational data assets for advanced analytics and data science teams.
• Follow established organizational standards for governance, security, access control, and data handling.

Education, Knowledge, Experience

• Minimum of 5-7 years’ hand-on experience.
• Background in programming, databases, data engineering, analytics engineering, or related technical disciplines; or degree in computer science, software engineering, information systems, economics, or other relevant engineering fields.
• Experience working with data warehousing, data marts, reporting environments, and cloud-based data platforms.
• Experience building ETL/ELT pipelines and supporting analytics-focused data solutions

 

Functional Competencies

• Knowledge of data and analytics frameworks supporting data lakes, warehouses, marts, and reporting.
• Strong understanding of data modeling principles and the ability to design solutions aligned with business objectives.
• Experience using AI for data engineering tasks such as design support, testing, documentation, structured prompting, and workflow improvement.
• Ability to work across the data lifecycle, from ingestion and transformation through delivery of trusted analytical data sets.
• Understanding of data quality, validation, monitoring, retention considerations, and operational support requirements.
• Ability to balance modern engineering practices with strong core data development fundamentals.
• Strong collaboration and communication skills when working with analysts, developers, and business stakeholders.

 

Technical Competencies

• Strong SQL and Python skills.
• Hands-on experience with Snowflake and ETL/ELT or data integration platforms; IICS experience is strongly preferred.
• Experience with data orchestration and workflow tools; familiarity with Airflow is a plus.
• Experience with data transformation and modeling tools such as dbt or equivalent frameworks.
• Familiarity with CI/CD practices, version control, and testing approaches for data solutions.
• Experience working with relational databases such as SQL Server and PostgreSQL.
• Ability to work with APIs and semi-structured data formats such as JSON and XML.
• Understanding of cloud and DevOps concepts is a plus.
• Ability to adapt to evolving tools, platforms, and AI-assisted engineering practices.