<p>Key Responsibilities</p> <ul> <li> <p>Design, build, and maintain scalable ETL/ELT pipelines to support analytics and machine learning workloads.</p> </li><li> <p>Develop and manage robust data models and warehouse structures that support self-service analytics and reporting.</p> </li><li> <p>Work with stakeholders across the business to understand data requirements and ensure data availability, accuracy, and usability.</p> </li><li> <p>Implement and monitor data quality and validation checks to maintain trust in our data assets.</p> </li><li> <p>Collaborate closely with data science and analytics teams to ensure data infrastructure supports model training and deployment.</p> </li><li> <p>Optimize data storage and query performance across cloud-based and relational systems.</p> </li><li> <p>Stay current with emerging data engineering tools and architectures and advocate for best practices across the team.</p> </li></ul> <p>Required Qualifications</p> <ul> <li> <p>5+ years of experience in data engineering, data infrastructure, or related fields.</p> </li><li> <p>Proficiency in SQL and at least one programming language (e.g., Python, Java, Scala).</p> </li><li> <p>Experience working with cloud data platforms (e.g., AWS Redshift, Snowflake, BigQuery).</p> </li><li> <p>Strong knowledge of data modeling, data warehousing, and building ETL/ELT pipelines.</p> </li><li> <p>Familiarity with modern data orchestration tools (e.g., Airflow, dbt).</p> </li><li> <p>Excellent communication and collaboration skills.</p> </li></ul> <p>Preferred Qualifications</p> <ul> <li> <p>Experience with real-time data streaming technologies (e.g., Kafka, Kinesis).</p> </li><li> <p>Familiarity with CI/CD for data pipelines and infrastructure-as-code (e.g., Terraform).</p> </li><li> <p>Experience supporting machine learning workflows and model deployment.</p> </li><li> <p>Background in insurance, financial services, or other highly regulated industries.</p> </li></ul>