Data Engineer

Key Responsibilities :

Design, build, and maintain scalable and efficient ETL / ELT data pipelines using a variety of tools and technologies.

Manage and optimize our data warehouse, preferably with experience in Snowflake.

Collaborate with business intelligence professionals to ensure data is structured for optimal performance in reporting and analytics, with a strong preference for experience with tools like Sigma Computing or similar BI platforms.

Monitor data pipelines for performance and reliability, and troubleshoot issues as they arise.

Implement data governance and security policies to ensure data integrity and compliance.

Work with cross-functional teams to understand data needs and deliver solutions that meet business requirements.

Stay up-to-date with industry trends and best practices in data engineering to continuously improve our data systems.

Requirements :

At least 2 years of professional experience as a Data Engineer or in a similar role.

In this role will Designing, building, and maintaining the data infrastructure and pipelines that power our analytics and business intelligence. You will work closely with data scientists, analysts, and business stakeholders to ensure data is accurate, accessible, and reliable.

Proven experience with building and managing data pipelines and data warehouses.

Strong proficiency in SQL and experience with database design and optimization.

Experience with a modern cloud data warehouse platform, preferably Snowflake.

Knowledge of and experience with BI tools like Sigma Computing, Tableau, Looker, or Power BI.

Familiarity with programming languages such as Python or Java for scripting and automation.

Experience with cloud platforms (e.g., AWS, GCP, Azure) and their data services.

Excellent problem-solving skills and a strong attention to detail.

Ability to work effectively with cross-functional teams and communicate complex data concepts to non-technical stakeholders.

Passion for creating robust and scalable data solutions and a commitment to data quality.