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.

• Design and develop scalable, distributed backend services that power ad delivery across FOX’s streaming platforms • Build and optimize systems responsible for ad pacing, forecasting, and delivery decisioning in near real-time • Collaborate with ML and Data Science teams to productionize algorithms for campaign optimization and performance • Improve system performance, reliability, and scalability to support hundreds of billions of ad impressions • Develop and maintain integrations with data pipelines, messaging systems, and real-time streaming frameworks • Own services end-to-end, including architecture, deployment, monitoring, and ongoing optimization • Contribute to best practices in system design, code quality, and operational excellence • Partner cross-functionally with product, infrastructure, and ad tech teams to evolve the platform

• Act as the primary strategic partner to client business owners, aligning technical data solutions with corporate goals while championing reliability and "clean data" for all consumers. • Drive the development of complex, large-scale data architectures and multi-year technology roadmaps, balancing technical innovation with cost efficiency and operational stability. • Oversee "team-of-teams" delivery performance, ensuring the quality and resilience of high-impact data pipelines while managing capacity planning and team velocity. • Lead the triage and resolution of critical platform incidents, taking full ownership of root-cause prevention and maintaining transparent communication with clients during recovery. • Implement robust governance frameworks and compliance controls, acting as the authoritative lead on risk mitigation and the primary technical contact for regulatory matters. • Champion an organisational culture of knowledge sharing, representing the firm at industry events and establishing best practices across communities of practice. • Influence long-term organizational health by driving platform efficiency, managing engineering budgets, and leading strategic hiring and retention initiatives.