> Markdown version of [/jobs/ext/543578-data-engineering-specialist-source-ai](https://www.wearedevelopers.com/jobs/ext/543578-data-engineering-specialist-source-ai). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Engineering Specialist - Source AI - **Company:** McKinsey & Company - **Location:** Atlanta, GA, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon S3, Cloud Storage, Continuous Integration, Information Engineering, Data Governance, Software Debugging, Github, JSON, Python (Programming Language), Performance Tuning, Systems Development Life Cycle, SQL Databases, Parquet, Snowflake, Semi-structured Data, Deployment Automation, Avro, Enterprise Integration, Data Management, Data Pipelines - **Published:** June 10, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=0b5c6d93ec2ca3a9 ## About the Role Do you have experience in Customer communication?, SnowPro certification, or equivalent deep hands-on Snowflake experience, is preferred 5+ years' experience in data engineering, including Snowflake, SQL (ANSI / Snowflake dialect), Python, APIs, data pipelines, testing, and debugging, designing and maintaining end-to-end data pipelines using Snowflake and cloud storage such as Amazon S3. Procurement, sourcing, spend analytics, or operations domain familiarity is a plus Expertise with core Snowflake capabilities such as Warehouses, Snowpipe, Streams, Tasks, and Secure Views Experience configuring and deploying analytics, data, or AI-enabled products in client environments, ideally with complex data and workflow requirements Ability to define scalable implementation patterns across clients while balancing customization with reuse Strong problem-solving across data quality, integration, workflow, and operational reliability issues Working knowledge of solution architecture, SDLC, secure and reliable deployment practices, and quality assurance Ability to lead and mentor engineers and external workers in fast-paced delivery settings Experience with CI/CD for data platforms (for example GitHub Actions), performance optimization, and migration from legacy environments to modern cloud data platforms is preferred Experience with semi-structured data formats such as JSON, Avro, and Parquet, and with secure integration to BI tools and downstream applications Knowledge of data governance and compliance requirements, including PII handling, GDPR, HIPAA, and data quality controls such as validation and anomaly detection Strong client communication skills with both technical and non-technical stakeholders ## Description Do you want to do work that matters, alongside supportive leaders who will help you grow faster than you ever thought possible? Are you a creative problem-solver who is energized by challenges? You've come to the right place. YOUR IMPACT You will drive day-to-day client implementation workstreams for Source AI deployments, including requirements tracking, issue management, data/input coordination, and execution follow-through. You will also support configuration of the product for each client context, including data setup, workflow configuration, output validation, and testing support. You'll partner closely with the senior technical lead to troubleshoot deployment issues and maintain pace across short implementation cycles. You will work in a client-facing, delivery and technical implementation role. You will drive day-to-day deployment execution, translate business needs into technical tasks, support configuration and testing, and keep delivery moving across multiple workstreams. Our Source AI team uses generative AI to analyze vast amounts of data, identify patterns, and create actionable insights within Procurement. Our team helps category managers unlock new levels of efficiency, innovation, and cost savings in their procurement and sourcing with an innovative and tailored solution. ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Innovating Developer Tools with AI: Insights from GitHub Next](https://www.wearedevelopers.com/videos/1268-innovating-developer-tools-with-ai-insights-from-github-next) - [Tips and Tricks for Working with JSON](https://www.wearedevelopers.com/videos/1229-tips-and-tricks-for-working-with-json) - [From event streaming to event sourcing 101](https://www.wearedevelopers.com/videos/91-from-event-streaming-to-event-sourcing-101) - [Introducing JSON Structure](https://www.wearedevelopers.com/videos/100219-introducing-json-structure) - [Bringing AI Model Testing and Prompt Management to Your Codebase with GitHub Models](https://www.wearedevelopers.com/videos/1536-bringing-ai-model-testing-and-prompt-management-to-your-codebase-with-github-models) ## Related Articles - [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) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know)