Senior AI Data Engineer
New
C
Cortica - NeurodevelopmentalHealthcare
CA, TX, NC, WA, ID, NV, AZ, CO, KS, AR, LA, AL, GA, FL, SC, TN, VA, MD, NJ, DE, IL, WI, MI, OH, MA, PA, NH, CTFull-TimeSenior
Salary160,000 - 200,000 USD per year
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Job Details
- Experience
- 5+ years of hands-on data engineering experience, including building and operating production data pipelines. 2+ years’ experience working with AI first development workflows. 4+ years’ experience with the following 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.
- Required Skills
- GraphQLNode.jsPythonSQLFlaskKafkaMicrosoft Power BISalesforceSnowflakeAzureRESTful APIsAWS Lambda
Requirements
- 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 — you've 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.
Responsibilities
- 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 (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.
- 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.
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