Senior Analytics Engineer (AI Insurance SaaS)

Responsibilities

• Lead the design, development, and implementation of a scalable, reliable, and efficient analytical data platform.
• Influence decisions on technology and tool selection for data ingestion, storage, transformation, and analysis for the analytics platform.
• Contribute to the overall data architecture and strategy, ensuring alignment with business needs and best practices.
• Build and maintain robust ETL/ELT pipelines to ingest, transform, and load data from various sources into the data warehouse (e.g., cloud storage, databases, APIs).
• Develop and optimize data models for analytical use cases, ensuring data quality, consistency, and accessibility.
• Establish and enforce data quality standards and processes to ensure data accuracy and integrity.
• Implement data governance policies and procedures to manage data access, security, and compliance for analytics use cases.
• Proactively identify and address data quality issues, working with stakeholders to resolve root causes.
• Partner with data scientists, analysts, and other engineers to understand their data needs and provide solutions.
• Effectively communicate technical concepts and designs to both technical and non-technical audiences.
• Mentor and guide data engineers, fostering a culture of learning and collaboration.
• Stay up-to-date on the latest trends and technologies in data engineering and analytics.
• Identify opportunities to improve existing data processes and tools.
• Proactively propose and implement innovative solutions to address data challenges.

Requirements

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
• 5+ years of experience in data engineering, with a focus on building analytical data platforms.
• Demonstrated experience with cloud data warehousing solutions (e.g. BigQuery, Snowflake, Redshift).
• Strong proficiency in SQL and experience with data modeling techniques (e.g., star schema, dimensional modeling).
• Experience building and maintaining ETL/ELT pipelines using tools like Dagster, Apache Airflow, dbt, or similar.
• Experience with programming languages such as Python, Java, or Scala.
• Experience with data governance and data quality tools and processes is highly desirable.
• Excellent problem-solving and analytical skills, along with passion for data and a commitment to data quality.