Description:

The Senior Data Engineer will be responsible for designing, building, and optimizing the data infrastructure.
This role involves developing scalable data pipelines, ensuring data quality, and enabling efficient data access for analytics and business insights.
The engineer will collaborate closely with data scientists, analysts, and software engineers to shape the data strategy and drive innovation.
Key responsibilities include leading database design, cloud deployment (Google Cloud, Cloud SQL, BigQuery), and API integrations.
The engineer will work with business stakeholders to identify and prioritize critical business problems that can be solved with data-driven solutions.
They will build and maintain automated pipelines that move data from diverse sources into the data warehouse, ensuring data quality and integrity.
The role requires deploying scalable data solutions to production and ensuring their reliability and performance meet business needs.
Collaboration with cross-functional teams is essential to align data solutions with broader organizational goals.
The engineer will implement and maintain data pipelines in production environments and regularly assess their performance for optimization.
Monitoring systems will be set up to track the performance of data products and provide actionable reports to stakeholders.

Requirements:

A minimum of 8 years of experience in data engineering or a similar role is required, with a proven track record of end-to-end solution delivery.
Candidates must have experience in building and deploying data pipelines and machine learning models at scale in production environments.
Demonstrated experience in moving data from multiple sources into centralized data warehouses is essential.
A Bachelor’s or Master’s degree in Computer Science, Data Science, or a related field is required.
Professional certifications in cloud technologies or machine learning frameworks are a plus.
Proficiency in programming languages such as Python, SQL, and API Development is necessary.
Experience with data engineering tools like BigQuery, dbt, Fivetran, Cloud SQL, ETL Pipelines, and Airflow is required.
Familiarity with version control systems like GitHub and cloud technologies such as Google Cloud, Docker, and CI/CD is essential.
Knowledge of data visualization tools like Looker Studio and Power BI is required.
Technical requirements include a Windows 11 operating system or macOS 13+, a minimum of 256GB storage space, 16GB RAM, a dual-core CPU, an HD webcam, a noise-cancelling headset, and a minimum internet speed of 50 Mbps.