Databricks Solution Architect

IBM; Atlanta (GA); Remote; USD 150,000 - 210,000; Full time

Job summary

IBM Consulting seeks a Databricks Solution Architect to define and lead end-to-end architecture for a large data modernization program. You will bring deep expertise across cloud-based engineering, Databricks platform, data migration, data modeling, data architecture, data engineering, and reporting, connecting governed data products to ML and GenAI solutions across hyperscalers.

You will design secure, scalable, and policy-compliant data architectures embedding AI capabilities into enterprise

Qualifications

• Databricks Certified Data Engineer or Machine Learning Professional.
• Advanced expertise in data/solution architecture, enterprise data migration, data modelling, lakehouse/data-product architecture, reporting, and cloud-based data engineering using Databricks and AWS.
• Hands-on experience with Spark (PySpark/Scala), Delta Lake, MLflow, Unity Catalog, and large-scale batch/streaming workloads.
• Experience with open table formats (Delta Lake, Apache Iceberg) and data-sharing patterns (Delta Sharing, Lakehouse Federation).

Responsibilities

• Define target data and AI architecture across AWS/Databricks and GCP ML/GenAI services.
• Lead data migration architecture including patterns, sequencing, cutover, and risk management.
• Own data modeling standards covering conceptual, logical, and physical models and lakehouse layers.
• Define data engineering patterns for ingestion, transformation, orchestration, CI/CD, observability, and cost control on cloud platforms.
• Architect reporting and analytics with semantic models and governed metrics.
• Design ML/GenAI lifecycle including feature engineering, model training/serving, and MLOps.
• Architect cross-cloud data movement across AWS, Azure, and GCP with secure patterns.
• Ensure data products from the Databricks lakehouse and SAP data via SAP BTP/BDC Connect.
• Define security, privacy, governance guardrails for AI use cases.
• Provide architectural oversight on initiatives like MySQL to Databricks Lakebase migration.
• Shape roadmap for advanced AI capabilities (agentic AI, frontier models).
• Lead architecture reviews and communicate decisions to execs and tech teams.
• Embed AI services, models, and orchestration with existing systems and data platforms.

Skills

Databricks

Spark PySpark

Delta Lake

MLflow

Unity Catalog

APIs

Event-driven architecture

Security & governance

GenAI experience

Education

Databricks Certification

Tools

Fivetran

Databricks Lakebase

Collibra

Erwin

ER Studio

Job description

Introduction

A career in IBM Consulting is built on long-term client relationships and close collaboration worldwide. You'll work with leading companies across industries, helping them shape their hybrid cloud and AI journeys. With support from our strategic partners, robust IBM technology, and Red Hat, you'll have the tools to drive meaningful change and accelerate client impact. At IBM Consulting, curiosity fuels success. You'll be encouraged to challenge the norm, explore new ideas, and create innovative solutions that deliver real results. Our culture of growth and empathy focuses on your long-term career development while valuing your unique skills and experiences.

Your role and responsibilities

Databricks Solution Architect to define and lead the end-to-end architecture for a large data modernization program. You will bring deep expertise across cloud-based engineering, Databricks platform, data migration, data modeling, data architecture, data engineering, and reporting, and connect governed data products to ML, GenAI, and advanced AI solutions across hyperscalers.

Responsibilities

• Define the target data and AI architecture across the AWS/Databricks data platform and GCP-based ML/GenAI services, and the integration patterns between them.
• Lead the data migration architecture: re-engineering approach, migration patterns, Integration & Ingestion patterns, sequencing, cutover, reconciliation, and risk management.
• Own data architecture and data modeling standards, covering conceptual, logical, and physical models, lakehouse/medallion layers, and data product design.
• Define data engineering patterns and standards for ingestion (Fivetran), transformation, orchestration, CI/CD, observability, and cost control on cloud-based platforms.
• Architect the reporting and analytics layer, including semantic models, governed metrics, and self-service access on top of data products.
• Design the ML/GenAI lifecycle: feature engineering, model training and serving, RAG and agentic patterns, MLOps/LLMOps, monitoring, and evaluation.
• Architect secure, scalable cross-cloud data movement and integration across hyperscalers (AWS, Microsoft Azure, GCP), selecting services based on fit, cost, and performance.
• Ensure the architecture uses data products from the Databricks lakehouse, including SAP-sourced data via SAP BTP / BDC Connect.
• Define security, data privacy, responsible AI, and governance guardrails for AI use cases.
• Provide architectural oversight on related initiatives, such as the Tier-1 application migration from MySQL to Databricks Lakebase.
• Evaluate and shape the roadmap for advanced AI capabilities (agentic AI, frontier models, and emerging superintelligence-class technologies) as they apply to the client.
• Lead architecture reviews and communicate design decisions to executive and technical stakeholders.
• Design end-to-end architectures that embed AI capabilities into enterprise applications and workflows, defining reference patterns for AI services, models, and orchestration layers that integrate with existing systems, APIs, and data platforms.
• Collaborate with stakeholders to translate business objectives into scalable, resilient, and policy-compliant designs, balancing vendor-managed services with custom development and meeting performance, security, compliance, and cost objectives.
• Drive governance for AI integration: prompts, guardrails, and lifecycle management, with observability, privacy, and responsible AI principles embedded from design through operations.

This job can be performed from anywhere in the US

Required technical and professional expertise

• Must be able to travel 4 days per week (mandatory).
• Databricks Certified Data Engineer or Machine Learning Professional, with proven expertise architecting enterprise-scale data, AI/ML, and Generative AI solutions.
• Advanced expertise in data/solution architecture, enterprise data migration, data modelling, lakehouse/data-product architecture, reporting, and cloud-based data engineering using Databricks and AWS.
• Hands-on experience with Spark (PySpark and/or Scala, Spark SQL), Delta Lake, MLflow, Unity Catalog, and large-scale batch and streaming workloads.
• Experience with open table formats, including Delta Lake and Apache Iceberg, and modern data-sharing patterns such as Delta Sharing and Lakehouse Federation.
• Experience architecting enterprise AI/GenAI solutions, including RAG, agents, MLOps/LLMOps, observability, guardrails, privacy, and responsible AI, with working knowledge of AWS, Azure, and GCP.
• Strong expertise in integration, security, and architecture leadership, including APIs, microservices, event-driven patterns, Kafka, and Amazon EventBridge/SQS, with strong technical and executive-level communication.

Preferred technical and professional experience

• Knowledge of SAP BTP, SAP Business Data Cloud, SAP-native data products, and SAP IS-U.
• Experience with Fivetran, Databricks Lakebase, OLTP-to-lakehouse patterns, Collibra, Erwin, or ER Studio.
• Advanced knowledge of AI orchestration, RAG/hybrid reasoning, microservices, and AI governance tooling.
• Experience with reporting and analytics architecture, including semantic layers and BI delivery on a lakehouse.
• Experience in the utilities or energy industry, with relevant cloud or Databricks architecture certifications.

IBM is committed to creating a diverse environment and is proud to be an equal-opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, gender, gender identity or expression, sexual orientation, national origin, caste, genetics, pregnancy, disability, neurodivergence, age, veteran status, or other characteristics. IBM is also committed to compliance with all fair employment practices regarding citizenship and immigration status.