Design, develop, and optimize ETL/ELT pipelines for both structured and unstructured data.
Mentor junior team members and engage in communities of practice to deliver high-quality data and AI solutions while promoting best practices, standards, and adoption of reusable patterns.
Partner with architects and stakeholders to influence and implement the vision of the AI and data pipelines while safeguarding the integrity and scalability of the environment.
Ingest and process large-scale datasets into the Enterprise Data Lake and downstream systems.
Curate and publish Data Products to support analytics, visualization, and machine learning use cases.
Collaborate with data analysts, data scientists, and BI teams to build data models and pipelines for research, reporting, and advanced analytics.
Apply best practices for data modeling, governance, and security across all solutions.
Partner with cross-functional teams to ensure alignment and delivery of high-value outcomes.
Monitor and fine-tune data pipelines for performance, scalability, and reliability.
Automate auditing, balance, reconciliation, and data quality checks to maintain high data integrity.
Develop self-healing pipelines with robust re-startability mechanisms for resilience.
Schedule and orchestrate complex, dependent workflows using tools like MWAA, Autosys, or Control-M.
Leverage CI/CD pipelines to enable automated integration, testing, and deployment processes.
Lead Proof of Concepts (POCs) and technology evaluations to drive innovation.
Develop AI-driven systems to improve data capabilities, ensuring compliance with industry best practices.
Implement efficient Retrieval-Augmented Generation (RAG) architectures and integrate with enterprise data infrastructure.
Implement data observability practices to proactively monitor data health, lineage, and quality across pipelines, ensuring transparency and trust in data assets.

Requirements

Bachelor’s or master’s degree in computer science or a related discipline.
5+ years of experience in data analysis, transformation, and development, with ideally 2+ years in the insurance or a related industry.
3+ years of experience developing and deploying large-scale data and analytics applications on cloud platforms such as AWS and Snowflake.
Strong proficiency in SQL, Python, and ETL tools such as Informatica IDMC for data integration and transformation (3+ years).
Experience designing and optimizing data models for Data Warehouses, Data Marts, and Data Fabric, including dimensional modeling, semantic layers, metadata management, and integration for scalable, governed, and high-performance analytics (3+ years).
3+ years of hands-on experience in processing large-scale structured and unstructured data in both batch and near-real-time environments, leveraging distributed computing frameworks and streaming technologies for high-performance data pipelines.
Strong technical knowledge (AI solution leveraging Cloud and modern solutions)
3+ years of experience in Agile methodologies, including Scrum and Kanban frameworks.
2+ years of experience in leveraging DevOps pipelines for automated testing and deployment, ensuring continuous integration and delivery of data solutions.
Proficient in data visualization tools such as Tableau and Power BI, with expertise in creating interactive dashboards, reports, and visual analytics to support data-driven decision-making.
Ability to analyze source systems, provide business solutions, and translate these solutions into actionable steps.

Benefits

health insurance
401(k) matching
flexible work hours
paid time off

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

ETLELTSQLPythonInformatica IDMCdata modelingdata integrationdata transformationdata observabilitycloud platforms

Soft Skills

mentoringcollaborationcommunicationleadershipproblem-solvinginnovationalignmentorganizational skillsadaptabilityanalytical thinking

Certifications

Bachelor’s degree in computer scienceMaster’s degree in computer science