Managing Consultant, Data Architect

IBM•, LA

1d•Remote

About The Position

The Managing Consultant, Data Architect is the principal technical authority for enterprise data and AI platforms across a portfolio of client engagements. The role designs target-state architecture and roadmaps, data modeling and integration patterns, security and governance architecture, and reference architectures and standards for consistent delivery. It spans Snowflake, Databricks, and cloud-native services on AWS, Azure, and GCP. The role leads architecture through delivery, from defining target states and workstreams to documenting trade-offs and reviewing implementation against approved designs. It leads discovery and executive advisory, provides business-first technical guidance, advises clients on AI readiness and data strategy, and architects platforms that support AI workloads while preserving data quality, structure, and governance. The position is accountable for technical outcomes across governed engagements. It sets direction for engagement architects and lead engineers, runs architecture reviews, authors practice reference architectures, leads solutioning and pre-sales, and mentors the practice's architecture bench through technical authority rather than line management. The role can be performed from anywhere in the US.

Requirements

Bachelor's degree in Computer Science, Engineering, or an equivalent field.
10+ years in data engineering, data warehousing, or solution architecture, including 4+ years owning end-to-end architecture on enterprise data platform engagements and serving as lead architect on at least three implementations.
Client-facing consulting or professional services delivery experience, including executive stakeholder engagement and advisory across concurrent engagements.
5+ years of advanced hands-on experience with at least one modern cloud data platform: Snowflake or Databricks; working knowledge of the other sufficient to design coexistence or migration architectures.
Deep expertise in enterprise data modeling, including dimensional, Data Vault, and 3NF, and target-state platform design across ingestion, transformation, semantic, and consumption layers, including data mesh and data product patterns.
Proven experience designing batch and streaming ingestion frameworks and selecting integration patterns such as CDC, event-driven, file-based, and API-based approaches according to client constraints.
Strong SQL and Python proficiency; hands-on experience with ELT and orchestration tools such as dbt, Matillion, Informatica, Airflow, or platform-native pipelines, sufficient to review, prototype, and unblock delivery work.
Experience with CI/CD and infrastructure as code for data platforms, including GitLab, GitHub Actions, or Azure DevOps and Terraform, covering pipelines, environment promotion, and automated testing.
Cloud platform experience on AWS, Azure, or GCP, including networking, IAM, storage, private connectivity, and data platform cost considerations.
Experience architecting platforms for AI and ML workloads, including feature and vector data, RAG patterns, and LLM integration within data pipelines.
Ability to assess client AI readiness and data maturity and translate findings into a phased architecture roadmap.
Experience defining and enforcing architectural standards, reference architectures, and reusable patterns across multiple delivery teams.
Demonstrated technical leadership, including setting technical direction, conducting architecture, design, and code reviews, and mentoring architects and engineers.
Track record owning technical delivery across complex, multi-workstream engagements, including estimation and risk management.
Track record contributing architecture, estimates, and technical narratives to proposals and statements of work.
Strong client-facing communication skills, including presenting and defending architecture decisions to executive and technical stakeholders.
Practical use of AI-assisted development tooling to improve design and delivery throughput.
Comfort working in Agile delivery environments.

Nice To Haves

SnowPro Advanced: Architect certification; Databricks Certified Data Engineer Professional or Solutions Architect credential. Holding both is a strong plus.
AWS Solutions Architect Professional, Azure Solutions Architect Expert, or GCP Professional Data Engineer certification.
Leadership of migrations from legacy warehouses such as Teradata, Oracle, SQL Server, or Hadoop to modern cloud platforms, including assessment, wave planning, and validation.
Experience designing governance frameworks, data quality controls, lineage, and cataloging across enterprise platforms such as Alation, Atlan, Collibra, or equivalent.
Experience with Kafka, Kinesis, EventBridge, or Event Hubs for operational and analytic data movement.
Applied AI experience in production, including MLOps practices, model serving, agentic data workflows, and operating AI workloads on data platforms.
Experience with FinOps for data platforms, including cost governance models, chargeback, and optimization programs across platform and cloud spend.
Regulated-industry experience in financial services, insurance, healthcare and life sciences, energy, or the public sector.
Technical community contributions through speaking, writing, open source, or internal enablement.

Responsibilities

Own end-to-end target-state architecture for enterprise data and AI platforms across concurrent engagements, including platform topology, environment strategy, security and governance models, and cost and performance architecture.
Lead architecture assessments, AI readiness evaluations, and data maturity evaluations; develop phased roadmaps with success metrics, investment cases, and cost guardrails; present and defend recommendations to client executives.
Design layered data models, including medallion, dimensional, Data Vault, and 3NF models, as well as semantic layers; select and justify integration patterns and record trade-offs in architecture decision records.
Author and maintain reference architectures, decision records, reusable patterns, and engineering standards for ingestion, transformation, orchestration, observability, governance, AI-ready data, CI/CD, and infrastructure as code.
Run architecture reviews for engagement architects and lead engineers, approve or redirect designs before build, and resolve cross-engagement design questions.
Set adoption direction for platform-native and AI capabilities, and determine how platforms fit within client estates, including coexistence and migration architectures.
Maintain technical accountability through delivery by decomposing architecture into workstreams, reviewing deliverables against approved designs, identifying architectural risk, scope drift, and technical debt, and proposing resolutions with project and practice leadership.
Own architecture, effort estimates, staffing plans, and technical narratives for proposals and statements of work; lead technical discovery and solution design during pursuit cycles.
Set technical direction for engagement teams; mentor architects, data engineers, and analytics engineers; support practice hiring, onboarding, and technical enablement.
Contribute accelerators, estimation models, and enablement content; represent the practice with platform partners and in technical communities.
Use AI tooling in design and delivery and establish standards for its use across engagement teams.