Senior Data Architect

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Mission AI Consulting is where veteran leadership meets cutting-edge ServiceNow innovation. We're seeking a Senior Data Architect to design and lead the build-out of our modern data platform powering AI/ML and Agentic AI initiatives. If you bring deep data architecture expertise, hands-on engineering skill, and the ability to grow a team, your next mission is waiting. Join a purpose-driven team that blends discipline with innovation.

Role Description

This is a full-time role for a Senior Data Architect to design and lead the build-out of our modern data platform spanning operational NoSQL systems (MongoDB), Lakehouse/data warehouse infrastructure (Databricks), and the pipelines that feed our AI/ML and Agentic AI initiatives. This is a hybrid architecture, consulting, hands-on engineering, and people-leadership role: you'll set data modeling and pipeline standards and lead a team of data engineers — including creating the training and upskilling plan that grows the team's capability in NoSQL data modeling, Lakehouse engineering, and AI/ML-ready data delivery. You'll report to the CTO as a senior/lead individual contributor with people-leadership responsibility.

Key Responsibilities

Strategic Consulting

• Own the end-to-end data architecture strategy for customers spanning operational stores (MongoDB), the data lake/lakehouse, and the data warehouse layer, aligning short-term delivery with a 12–24 month roadmap.
• Consult, evaluate, and recommend emerging data and AI infrastructure technologies, and produce architecture decision records for major platform choices for large enterprise customers.
• Design efficient, scalable MongoDB schemas (embedding vs. referencing), indexing strategies, and aggregation pipelines for high-throughput application and integration workloads.
• Define sharding, replication, and high-availability strategies; own performance tuning, capacity planning, and backup/recovery approach.
• Partner with application and integration teams to integrate MongoDB with microservices, APIs, and downstream analytics/ETL pipelines feeding the lakehouse.
• Architect and help build scalable batch and streaming pipelines (PySpark, Spark SQL, Delta Live Tables / Workflows, Structured Streaming) that move data from operational systems, including MongoDB, into the lakehouse.
• Stand up CI/CD for data pipelines (Databricks Repos/Jobs, Git-based workflows, IaC) and instill DataOps discipline for customer teams.
• Design and curate data pipelines and feature stores that make trusted, well-governed data available for AI/ML and Agentic AI use cases (e.g., ServiceNow Now Assist / AI Agent Studio, RAG pipelines, MongoDB Atlas Vector Search).

Team Leadership & Talent Development

• Lead, mentor, and grow a team of data engineers — setting technical direction and owning delivery quality across the team's projects.
• Build and maintain a structured training and upskilling plan for the data engineering team, covering MongoDB data modeling, Databricks/Spark engineering, data governance, and applied AI/ML data patterns; track skill progression and certification goals (e.g., MongoDB Certified, Databricks Certified Data Engineer).
• Define hiring profiles and interview loops to scale the team and create onboarding paths for new engineers.
• Act as a technical liaison to clients and internal stakeholders, translating business and AI/ML requirements into practical data solutions and clearly communicating architectural trade-offs.

Minimum Qualifications

• 6-7 years in data engineering/data architecture, including 3+ years in a lead or architect-level role.
• Hands-on production experience with MongoDB: schema design, indexing, aggregation framework, sharding, and replication.
• Working knowledge of Databricks (or a comparable lakehouse platform) — Spark/PySpark, Delta Lake, pipeline orchestration — sufficient to guide architecture decisions and mentor engineers, even if not the deepest hands-on operator.
• Strong SQL and Python; experience building and operating ETL/ELT pipelines at scale.
• Practical exposure to AI/ML data patterns — feature pipelines, vector/embedding storage, RAG data preparation, or MLOps — and genuine interest in staying current as the space evolves.
• Strong communication skills, comfortable presenting architecture and trade-offs to both technical and executive/client audiences.

Preferred Qualifications

• Databricks Certified Data Engineer / Data Architect, and MongoDB Certified Developer or DBA.
• Experience with cloud platforms (AWS, Azure, or GCP) and Infrastructure-as-Code (Terraform).
• Experience with MongoDB Atlas Vector Search or another vector database used for RAG/AI Agent workloads.
• Background in a consulting or professional-services environment, or experience with ServiceNow data/integration patterns (a plus, not required).
• Experience designing and delivering internal training curricula or a formal upskilling/L&D program for a technical team.

U.S. Veterans Encouraged to Apply

Mission AI Consulting is proud to be veteran-led. U.S. Veterans are strongly encouraged to apply.

Equal Opportunity

Mission AI Consulting is an equal opportunity employer. We consider all qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other characteristic protected by applicable law.