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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Scientist - **Company:** Wise Equation Solutions - **Location:** Greensboro, NC, United States - **Contract:** Permanent contract - **Skills:** Amazon Web Services, Confluence, JIRA, Databases, Computer Engineering, Extract Transform Load (ETL), Data Masking, Data Systems, Data Visualization, Amazon DynamoDB, Python (Programming Language), Machine Learning, Power BI, DataOps, Amazon Simple Notification Service (SNS), Software Engineering, SQL Stored Procedures, SQL Databases, Systems Integration, Tableau (Software), Data Processing, Real Time Systems, Snowflake, Gitlab, Data Lakes, Pyspark, Information Technology, Wikis, Dataiku, Amazon Simple Queue Service (SQS), Splunk, Data Pipelines, Api Management, Alteryx, Amazon Redshift, Databricks, Programming Languages - **Published:** June 29, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=9e83fcc84b022f15 ## About the Role Do you have experience in Wiki systems?, Minimum Education Requirement:- This position requires, at a minimum of a bachelor's degree in computer science, computer information systems, information technology, relevant engineering, (computer engineering, software engineering, electronic engineering or related) or a combination of education and experience equating to the U.S. equivalent of a Bachelor's degree in one of the aforementioned subjects. ## Description * Create scalable data pipelines using Glue, Eventbridge, DynamoDB to process life insurance data and support data modeling for policy records, claims, actuarial datasets. * Write and optimize ETL and reconciliation workflows using PySpark and Python to transform raw policyholder data into analytics-ready datasets for reporting and decision-making. * Build and implement event-driven data solutions on AWS, leveraging SQS, SNS, and API integrations to enable real-time processing of insurance transactions and policy events * Implement snowflake data masking and row level security policies to ensure sensitive policyholder and beneficiary data are protected. * Create Splunk data pipelines and PowerBI dashboards to ingest, index and analyze structured and unstructured life insurance data including policy transactions, claims events enabling real-time operational monitoring. * Build flow zones and manifest file generator in Dataiku to develop data processing logic that meets life insurance business and regulatory requirements * Write and optimize ETL workflows using data-oriented programming languages such as PySpark and Python to extract and transform raw policyholder data and support machine learning model development by preparing high-quality, analytics-ready datasets for reporting and decision-making. * Write SQL queries and stored procedures in Amazon redshift to support life insurance reporting, premium reconciliation, and claim analytics and implement optimized data modeling techniques for analytical workloads. * Develop end-to-end data workflows in Dataiku, integrating multiple sources to extract and analyze life insurance data such as policy administration systems, claims databases and actuarial feeds for business insights. * Write and optimize Delta Lake table implementations in Databricks to support efficient storage, ACID transactions, and time travel queries on life insurance datasets. * Build and maintain alteryx server scheduled workflows to automate end-to-end data processing pipelines for life insurance data and enable data visualization for claims reporting on Tableau. * Work on Jira tickets for the sprint plan and utilize Gitlab repositories and confluence and participate in retrospective meetings ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Improving quality with Agentic AI with Rovo Dev and Xray](https://www.wearedevelopers.com/videos/2005-improving-quality-with-agentic-ai-with-rovo-dev-and-xray) - [WeAreDevelopers LIVE - Modern DevOps for IoT Devices and More](https://www.wearedevelopers.com/videos/1805-wearedevelopers-live-modern-devops-for-iot-devices-and-more) - [Living Documentation That Can't Die](https://www.wearedevelopers.com/videos/2025-living-documentation-that-can-t-die) - [Are Code Reviews Worth It? Insights from 16 Years of Review Data](https://www.wearedevelopers.com/videos/1135-are-code-reviews-worth-it-insights-from-16-years-of-review-data) - [Empowering Retail Through Applied Machine Learning](https://www.wearedevelopers.com/videos/976-empowering-retail-through-applied-machine-learning) ## Related Articles - [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know) - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk) - [Data Analyst Salary in the UK](https://www.wearedevelopers.com/magazine/278-data-analyst-salary-in-the-uk) - [A Guide to Green Tech and Green IT Careers](https://www.wearedevelopers.com/magazine/374-a-guide-to-green-tech-and-green-it-careers)