Analytics Engineer

This role is for one of the Weekday's clients

If you are passionate about data engineering and turning complex datasets into actionable insights, we want to hear from you!

About the Role

This role provides a unique opportunity to shape data strategy and influence product direction through data-driven insights.

Requirements

• Design & Implement Scalable Solutions – Architect and develop end-to-end analytics systems, including data ingestion, modeling, and visualization, ensuring robustness and scalability for large data volumes.
• Data Modeling & Pipeline Development – Create and maintain dimensional data models, build and optimize ETL/ELT pipelines, and automate reporting using modern data stack technologies (e.g., dbt, Airflow, Redshift, Python, Sisense, Athena, Apache Iceberg).
• Collaborative Data Analysis – Partner with product teams to instrument and analyze user behavior data, translating business needs into technical requirements and delivering insightful analytics.
• Data Quality & Monitoring – Implement data quality checks and monitoring frameworks to ensure accuracy, reliability, and consistency.
• Documentation & Knowledge Sharing – Develop and maintain comprehensive documentation for data models, business logic, and analytical workflows.
• Mentorship & Leadership – Guide and mentor junior engineers and analysts, fostering a culture of data engineering excellence and best practices.

• 5+ years of experience in analytics engineering, data engineering, or similar roles within SaaS or enterprise settings.
• Advanced SQL proficiency, with a strong track record of designing and implementing scalable data models.
• Hands-on experience with BI and visualization platforms (e.g., Looker, Sisense, Tableau, Power BI).
• Proficiency in dbt (or similar transformation tools) for data transformation and pipeline automation.
• Experience working with cloud data warehouses (e.g., Redshift, Snowflake, BigQuery).
• Strong Python skills for data manipulation and analysis.
• Excellent communication and collaboration skills, with the ability to translate complex technical concepts for non-technical stakeholders.

• Experience in modeling complex datasets for BI and analytics.
• Familiarity with Airflow (or similar ETL tools).
• Knowledge of data governance and security best practices.
• Exposure to real-time data streaming technologies (e.g., Kafka, Kinesis).
• Understanding of machine learning and statistical modeling techniques.