Design, build, and maintain reliable data pipelines and platforms that power analytics, reporting, and data products across a Property & Casualty auto insurance business.
Compensation
Salary: $70,000 - $120,000 annually based on experience
Responsibilities
Build and maintain scalable batch and streaming data pipelines (ingestion, transformation, validation, and delivery).
Integrate data from core insurance systems (policy administration, billing, claims, Contact management system).
Model and curate analytical datasets (e.g., policy, quote, exposure, premium, loss, claim, payment, reserve, subrogation, salvage).
Implement data quality checks, anomaly detection, and reconciliation for critical metrics.
Develop and maintain a data lake/warehouse (schema design, partitioning, performance tuning, cost optimization).
Requirements
Bachelor's degree in computer science or technology related
Design test strategies for data pipelines, validating accuracy, completeness, schema consistency and transformation logic across ETL and ELT workflows in Snowflake
Build new automated data quality checks in dbt tests and Python, based on specific use cases and business requirements, and run them inside pipeline CI/CD
Extend the existing dbt and Python data quality tests that are already in place
Build and maintain automated test suites (unit, integration, regression and end-toend) for applications
Validate orchestration workflows in Airflow or Dagster for correctness, idempotency and failure handling
Assists in implementing data architecture for a sub-domain, ensuring assigned data assets are properly organized, structured, and secured.
Responsibilities
Works with Product owners, Business stewards, Business Intelligence Engineers, Data Scientists and end users to understand data consumers’ needs and develop data products/ data solutions within assigned sub-domain
Explores Data Sources by working with Application owners to confirm datasets to be extracted
Designs Data models for Source data products and Master data products including schema design, table definitions, and column specifications
Designs and implements semantic models following established standards, supporting BI reporting, AI applications, and conversational analytics use cases
Documents Data Architecture artifacts for assigned data products
Requirements
Bachelor’s degree in computer science, data science, engineering, or related field
Individual contributor role focused on leveraging framework and platform capabilities to develop high-quality data pipelines and analytic solutions for data-driven decision-making.
Responsibilities
Develops and operates modern data engineering solutions to meet key business objectives
Contributes to generic frameworks for data ingestion, data processing, data integrations, and data governance
Follows best practices for software design, architecture, and implementation
Conducts code reviews as a core expectation of the role, providing feedback on logic, design patterns, and correctness with constructive commentary
Leverages monitoring tools/processes to ensure platform stability and operational SLAs
Requirements
Bachelor’s Degree or higher in Computer Science, Statistics, Business, Information Technology, or related field
Data Scientist working on analytical tasks and projects on huge amounts of data to support data-driven business decisions and development.
Responsibilities
Assist the implementation of next-generation techniques such as weak learning, time series, NLP supported by AI and agents, including tests, validation and delivery
Explore and implement innovative and advanced statistical approaches to improve and transform solutions, including Large Language Model concepts and prompt engineering techniques to solve business challenges
Provide support and explainability to technical partners as well as business partners to drive adoption
Create documentation, compelling presentations and visualizations to explain complex technical algorithms and impacts into understandable business language
Requirements
Bachelor/Master or PhD degree (statistics, mathematics, physics, economics, biology or a similar field)
A hands-on DataOps and Data Quality Engineer role focusing on Microsoft Fabric engineering, automated data quality, monitoring, and operational reliability.
Responsibilities
Design and implement automated data quality controls within Microsoft Fabric to ensure reliable and trustworthy reporting data
Translate data quality and governance requirements into executable validation rules, monitoring checks and quality gates
Build automated checks for data enough, completeness, duplicates, schema changes, data volumes, key-field validity and other agreed business rules
Develop centralized monitoring and logging for pipeline runs, source arrival times, row counts, errors and data quality results
Create alerting and operational views to identify missing data, abnormal volume drops, failed loads and broken dependencies before they impact KPI reporting
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