Data Engineer

• Key Responsibilities

Design, build, and maintain scalable ETL/ELT pipelines to support analytics and machine learning workloads.

• Develop and manage robust data models and warehouse structures that support self-service analytics and reporting.

• Work with stakeholders across the business to understand data requirements and ensure data availability, accuracy, and usability.

• Implement and monitor data quality and validation checks to maintain trust in our data assets.

• Collaborate closely with data science and analytics teams to ensure data infrastructure supports model training and deployment.

• Optimize data storage and query performance across cloud-based and relational systems.

• Stay current with emerging data engineering tools and architectures and advocate for best practices across the team.

• Required Qualifications

5+ years of experience in data engineering, data infrastructure, or related fields.

• Proficiency in SQL and at least one programming language (e.g., Python, Java, Scala).

• Experience working with cloud data platforms (e.g., AWS Redshift, Snowflake, BigQuery, Databricks, Azure).

• Strong knowledge of data modeling, data warehousing, and building ETL/ELT pipelines.

• Familiarity with modern data orchestration tools (e.g., Airflow, dbt).

• Excellent communication and collaboration skills.

• Preferred Qualifications

Experience with real-time data streaming technologies (e.g., Kafka, Kinesis).

• Familiarity with CI/CD for data pipelines and infrastructure-as-code (e.g., Terraform).

• Experience supporting machine learning workflows and model deployment.

• Background in insurance, financial services, or other highly regulated industries.