• Responsible for the foundational data infrastructure that powers all of SailPoint's AI-driven insights and intelligent features. • Build and scale the platform that collects, processes, and enriches a vast identity data. • Designed to intelligently collect, transform, normalize, correlate, summarize, and store customer-specific and third-party data. • Provide leverage for R&D organization, ensuring consistency, quality, and speed in delivery. • Deliver significant new platform capabilities that are used by multiple product teams to launch AI-powered features.

• Set the technical direction for Seer’s data platform — define the architecture, the standards, and the roadmap that other engineers build against. • Architect Seer’s data platform end-to-end across BigQuery, Dataflow, Airflow, Cloud Run, Cloud Functions, and Cloud Storage, making the cost, latency, and freshness tradeoffs that let us scale without surprises. • Set the standard for how we model and serve data, from dbt project structure to LookML, Looker explores, and embedded Looker experiences inside client portals and internal tools. • Mentor data engineers and analysts across the team — pair on tough problems, lead architectural reviews, leave PR comments that make people better (not just their code), and run the occasional workshop on TDD, git internals, or LLM-augmented engineering. • Champion engineering practice — TDD, modular Pytest suites, clean Git workflows, and CI/CD pipelines with multi-stage deploys. Make the bar visible, then help everyone meet it. • Tune what’s expensive: hunt down the slow query, the redundant DAG, the leaky storage policy. Cut BigQuery costs and dashboard load times, and bring the team along with you. • Leverage tools like ChatGPT, Claude, Gemini, and prompt-evaluation harnesses to accelerate research, code review, and pipeline development — and design AI-driven workflows that scale Seer’s engineering throughput. • Document and teach. Write playbooks that survive system evolution, decision records that explain *why* not just *what*, and onboarding paths that compound across new hires.

Role Description Cortica is looking for a Senior AI Data Engineer to join its growing team! The Senior AI Data Engineer will serve as both architect and builder of our data ecosystem. Every initiative will follow a complete engineering lifecycle: - Gathering stakeholder requirements - Designing the solution - Building and testing it - Shipping it to production This role will work across data lakes, analytics pipelines, and lightweight application development— the multi-disciplinary data equivalent of a full-stack developer. The Senior AI Data Engineer will work closely with the data science, finance, and clinical operations teams to design intelligent, automated data solutions that power care decisions, financial planning, and operational efficiency. AI augmentation is not optional — it is the standard working mode. What will you do? - Full Lifecycle Project Ownership: - Engage stakeholders directly to gather, clarify, and document project requirements. - Translate requirements into architected data solutions: choose the right storage, pipeline, modeling, and delivery approach for each problem. - Own testing end-to-end — unit tests, data quality checks, reconciliation, and integration tests before anything reaches production. - Deploy solutions to production and monitor post-deployment health, iterating rapidly based on real-world feedback. - AI-First Data Engineering: - Run parallel AI coding sessions (Claude Code, Cursor, Codex) across different facets of a pipeline simultaneously — orchestrate, verify, and integrate the outputs. - Build and maintain context files (CLAUDE.md equivalents) for data projects that encode schema conventions, pipeline patterns, and institutional knowledge — making every future AI session smarter. - Design verification loops: automated data quality checks, dbt tests, CI hooks, and pipeline monitors that give AI agents concrete feedback on correctness. - Build MCP (Model Context Protocol) or equivalent integrations to connect AI agents directly to Snowflake, Amazon Athena, Postgresql, MySql, Power BI APIs, Salesforce, and internal tooling. - Prefer frontier models for complex architectural decisions and rely on AI acceleration to dramatically increase engineering throughput. - Data Platform & Pipeline Engineering: - Design and build complex, reliable data pipelines ingesting from AWS, Azure, Salesforce, MuleSoft, and multiple third-party APIs into our AWS Data Lake and Snowflake warehouse. - Implement and evolve data models using Kimball methodology to support financial, operational, and clinical analytics. - Optimize pipeline performance, manage data quality, and perform root-cause analysis on data anomalies — internal and external. - Develop and maintain orchestration workflows in Python, and AWS Glue. - Continuously evolve the data schema as business and engineering requirements change. - Analytics & Reporting Enablement: - Build and support Power BI data models and reports; empower analytics team members to self-serve on a reliable data foundation. - Work with data analysts and data scientists to build reusable, well-documented pipeline components they can extend independently. - Deliver data products that drive clinical care decisions, financial planning, and operational performance improvements. - Application Development: - Build lightweight internal data applications and tooling where needed; data entry interfaces, operational dashboards, automation scripts that bridge the gap between data pipelines and end users. - Design for agentic workflows: build AI-powered data tools accessible via web interfaces or Slack that surface insights proactively. - Integrate with Salesforce Health Cloud and other platforms using APIs and event-driven patterns. - Security, Governance & Collaboration: - Ensure data security and HIPAA compliance in all pipeline and application work. Partner with IT to enforce data governance standards. - Document decisions, tradeoffs, and architecture clearly so that future engineers (and AI agents) can build on your work effectively. - Collaborate across IT, finance, clinical operations, and data science — acting as the connective tissue between data infrastructure and business outcomes. Qualifications - 5+ years of hands-on data engineering experience, including building and operating production data pipelines. - Expert-level Python skills for ETL, pipeline orchestration, and automation. - Deep SQL proficiency — query optimization, data modeling, stored procedures. - 2+ years’ experience working with AI first development workflows. - 4+ years’ experience with AWS (S3, Glue, Lambda, Redshift), and/or Azure big data services. - 1+ year of experience with Snowflake. - 2+ years of experience with orchestration frameworks. - 2+ years of Salesforce experience with Apex and configurations. - Experienced with Kimball dimensional modeling — built star schemas and conformed dimensions in production. - Power BI (or equivalent BI tool) experience — data model design and report development. - API integration experience — REST, GraphQL, event streaming (Kafka, Kinesis, or similar). - Application development literacy — comfortable building lightweight web tooling (Python/Flask, Node, or similar) to complement data products. Benefits - Medical, dental, and vision insurance - 401(k) plan with company matching and rapid vesting - Paid holidays and wellness days - Life insurance - Disability insurance options - Tuition reimbursements for professional development and continuing education - Referral bonuses Compensation The base pay range for this opening is $160,000 to $200,000. According to your skill level, relevant experience, education level, and location, you will receive compensation that fits appropriately within the range.

Role Description The Senior AI Data Engineer will serve as both architect and builder of our data ecosystem. Every initiative will follow a complete engineering lifecycle: - Gathering stakeholder requirements - Designing the solution - Building and testing it - Shipping it to production This role will work across data lakes, analytics pipelines, and lightweight application development— the multi-disciplinary data equivalent of a full-stack developer. The Senior AI Data Engineer will work closely with the data science, finance, and clinical operations teams to design intelligent, automated data solutions that power care decisions, financial planning, and operational efficiency. AI augmentation is not optional — it is the standard working mode. Qualifications - 5+ years of hands-on data engineering experience, including building and operating production data pipelines. - Expert-level Python skills for ETL, pipeline orchestration, and automation. - Deep SQL proficiency — query optimization, data modeling, stored procedures. - 2+ years’ experience working with AI first development workflows. - 4+ years’ experience with AWS (S3, Glue, Lambda, Redshift) and/or Azure big data services. - 1+ year of experience with Snowflake. - 2+ years of experience with orchestration frameworks. - 2+ years of Salesforce experience with Apex and configurations. - Experience with Kimball dimensional modeling — building star schemas and conformed dimensions in production. - Power BI (or equivalent BI tool) experience — data model design and report development. - API integration experience — REST, GraphQL, event streaming (Kafka, Kinesis, or similar). - Application development literacy — comfortable building lightweight web tooling (Python/Flask, Node, or similar) to complement data products. - Reside in one of the specified states. Requirements - Engage stakeholders directly to gather, clarify, and document project requirements. - Translate requirements into architected data solutions: choose the right storage, pipeline, modeling, and delivery approach for each problem. - Own testing end-to-end — unit tests, data quality checks, reconciliation, and integration tests before anything reaches production. - Deploy solutions to production and monitor post-deployment health, iterating rapidly based on real-world feedback. - Run parallel AI coding sessions across different facets of a pipeline simultaneously — orchestrate, verify, and integrate the outputs. - Build and maintain context files for data projects that encode schema conventions, pipeline patterns, and institutional knowledge. - Design verification loops: automated data quality checks, dbt tests, CI hooks, and pipeline monitors. - Build MCP (Model Context Protocol) or equivalent integrations to connect AI agents directly to various platforms. - Design and build complex, reliable data pipelines ingesting from multiple sources into our AWS Data Lake and Snowflake warehouse. - Implement and evolve data models using Kimball methodology to support analytics. - Optimize pipeline performance, manage data quality, and perform root-cause analysis on data anomalies. - Develop and maintain orchestration workflows in Python and AWS Glue. - Continuously evolve the data schema as business and engineering requirements change. - Build and support Power BI data models and reports; empower analytics team members to self-serve on a reliable data foundation. - Work with data analysts and data scientists to build reusable, well-documented pipeline components. - Deliver data products that drive clinical care decisions, financial planning, and operational performance improvements. - Build lightweight internal data applications and tooling where needed. - Design for agentic workflows: build AI-powered data tools accessible via web interfaces or Slack. - Ensure data security and HIPAA compliance in all pipeline and application work. - Document decisions, tradeoffs, and architecture clearly. - Collaborate across IT, finance, clinical operations, and data science. Benefits - Medical, dental, and vision insurance. - 401(k) plan with company matching and rapid vesting. - Paid holidays and wellness days. - Life insurance and disability insurance options. - Tuition reimbursements for professional development and continuing education. - Referral bonuses.