Lead Data Architect

Overview

Summary

The Lead Data Architect will design, build, and operate enterprise data platforms that power GenAI and AI/ML use cases. This is a highly technical, hands-on role responsible for data platform architecture, end-to-end data engineering, ML/LLM pipeline design, production model onboarding, and delivery of scalable Databricks- centric solutions across cloud environments.

Responsibilities

What You'll Be Doing:

• Architect and implement enterprise data platforms (batch + streaming) optimized for ML, LLMs, and GenAI workloads.
• Lead design and hands on implementation of Databricks workspaces, Unity Catalog, Delta Lake design patterns, cluster policies, and performance tuning.
• Build and own end to end data pipelines (ingest, transform, feature engineering, serving) using PySpark, Databricks Jobs, Spark SQL, Delta Lake, and orchestration tools.
• Design and operationalize model training, fine tuning (LLM), evaluation, deployment, and monitoring pipelines (MLOps/RAG/CAG) integrating Databricks MLflow, CI/CD, and infra-as-code.
• Implement vectorless and vectorization/embedding pipelines, vector store integrations, and retrieval layers for RAG (FAISS, Pinecone, Weaviate, Milvus).
• Define data schemas, governance, lineage, access controls, and data product APIs; implement Unity Catalog or equivalent for centralized governance.
• Drive cost/performance optimization for storage, compute (spot/preemptible),and query patterns. 
• Collaborate with engineers, data scientists, product owners, and security to translate business needs into production GenAI solutions. 
• Mentor and lead engineering teams; conduct architecture reviews, code reviews, and run technical deep dives. 
• Implement observability for data and ML pipelines (metrics, logging, data quality tests, alerting). 
• Create reproducible experiment tracking, model registry, and rollout strategies (canary, shadow testing, rollback). 
• Stay current on GenAI/LLM architectures and evaluate/introduce new tooling and frameworks.

Qualifications and Education

Required Qualifications:

• BA or BS degree in CS, Computer Engineering, Information Technology or a related field.
• 8+ years hands on experience in data engineering/platform architecture; 3+ years in an architect or lead role.
• Candidate must hold an active AWS Certified Machine Learning – Specialty certification or equivalent AWS certification.
• Proven, hands on Databricks experience (designing workspaces, Delta Lake, performance tuning, productionizing Spark jobs).
• Deep Spark + PySpark expertise and experience with Databricks Runtime.
• Strong experience building ML/LLM pipelines and operationalizing models (training, fine tuning, serving).
• Practical experience with vector embeddings, semantic search, and RAG architectures.
• Solid Python expertise and common ML libraries (PyTorch, TensorFlow, Hugging Face transformers) and MLflow.
• Cloud platform experience (AWS strongly preferred).
• Experience with containerization and orchestration while leveraging open source libraries for unstructured and structured data processing, serving/inference.
• Strong SQL skills; experience with distributed query/warehouse systems and parquet/AVRO/Delta formats.
• CI/CD and infra-as-code experience (Terraform, GitOps, Jenkins/GitHub Actions/GitLab CI).
• Data governance, security, and IAM experience; experience implementing row/column level access controls and data lineage.
• Demonstrated ability to design for scalability, reliability, and cost efficiency. 

Compensation

The proposed salary range for this role is $****** to $******* USD. The salary range provided is a good faith estimate representative of all experience levels. Karsun considers several factors when extending an offer, including but not limited to, the role, function and associated responsibilities, a candidate’s work experience, location, education/training, and key skills.