Sr. Consultant, Data Architect

IBM•, LA

1d•Remote

About The Position

We are looking for a Sr. Consultant, Data Architect to join a growing team of experts. This role owns the technical architecture of client engagements on modern cloud data platforms, including Snowflake and Databricks alongside cloud-native services on AWS, Azure, and GCP. Work spans target-state platform design, data modeling, ingestion and transformation patterns, security and governance design, and engineering standards that support consistent delivery from initial design through production. The role combines architecture and hands-on delivery. The consultant designs and delivers enterprise data platforms end to end, leads client discovery to translate business goals into technical requirements, and documents the trade-offs behind design decisions. They use AI tooling in their work, advise clients on AI in their data strategy, and architect platforms for AI and analytics workloads while maintaining strong data quality, structure, and governance. The consultant carries technical leadership and accountability for each engagement’s technical outcome: ensuring the architecture is sound, the team builds to it, and what ships meets the design. They serve as the primary technical contact for client stakeholders, set technical direction for delivery teams, lead design and code reviews, and mentor engineers. The role is suited to someone comfortable with the variety of a consulting environment. It can be performed from anywhere in the US.

Requirements

Bachelor's degree in Computer Science, Engineering, or an equivalent field.
8+ years in data engineering, data warehousing, or solution architecture, including 2+ years as lead architect on enterprise data platform engagements.
Client-facing consulting or professional services delivery experience, including leading discovery and design sessions with technical and business stakeholders.
3+ years of hands-on experience with at least one modern cloud data platform: Snowflake or Databricks.
Enterprise data modeling experience across medallion, dimensional, and Data Vault or 3NF approaches, and target-state platform design spanning ingestion, transformation, semantic, and consumption layers.
Proven experience designing batch and streaming ingestion frameworks and choosing integration patterns based on client constraints.
Strong proficiency in SQL and Python; hands-on experience with ELT and orchestration tools such as dbt, Matillion, Informatica, Airflow, or platform-native pipelines.
CI/CD experience for data platforms using GitLab, GitHub Actions, or Azure DevOps, including pipelines, merge request workflows, environment promotion, and automated testing.
Cloud platform experience on AWS, Azure, or GCP, including storage, IAM, networking and private connectivity, secrets management, and data-platform cost considerations.
Experience designing platforms for AI and ML workloads, including feature and vector data and LLM integration within data pipelines.
Demonstrated technical leadership, including setting technical direction, conducting design and code reviews, and mentoring engineers at earlier career stages.
Strong client-facing communication skills, including presenting and defending architecture decisions to technical and executive 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 or Data Engineer certification; Databricks Certified Data Engineer Professional certification or equivalent.
Experience with both Snowflake and Databricks, including coexistence or migration architectures, and Apache Iceberg or other open table formats.
AWS Solutions Architect, Azure Solutions Architect Expert, or GCP Professional Data Engineer certification.
Infrastructure as code experience with Terraform, environment strategy, and observability and monitoring for data platforms.
Experience migrating legacy warehouses such as Teradata, Oracle, SQL Server, or Hadoop to cloud platforms, including assessment, wave planning, and validation.
Data governance and security architecture experience, including governance frameworks, data quality controls, lineage, and cataloging tools such as Alation, Atlan, or Collibra.
Experience with Kafka, Kinesis, EventBridge, or Event Hubs for operational and analytic data movement.
Exposure to MLOps, model serving, RAG and agentic data patterns, and Snowflake Cortex or Databricks Mosaic AI.
Regulated-industry experience in financial services, insurance, healthcare and life sciences, energy, or the public sector.
Experience contributing architecture, estimates, and technical narrative to proposals and statements of work.

Responsibilities

Lead discovery sessions with client stakeholders to clarify business goals, technical requirements, and constraints, and translate them into production-ready data platform architecture and a phased delivery plan.
Design platform topology, environment strategy, security and governance models, and cost and performance architecture.
Design layered data models, including medallion, dimensional, and Data Vault approaches, and semantic layers for BI, application, and AI consumption.
Select integration patterns such as batch, CDC, event-driven, file-based, API, and data sharing based on client constraints; document trade-offs in architecture decision records.
Lead model design, materialization strategy, testing, and documentation in dbt or an equivalent framework; establish CI/CD, coding standards, and peer review.
Architect integrations between data platforms and cloud services, including object storage, IAM, networking and private connectivity, secrets management, and managed processing services.
Evaluate and adopt platform-native capabilities where appropriate, and design data foundations for AI and ML workloads.
Own technical delivery to production by decomposing architecture into workstreams, reviewing implementation against the design, monitoring pipeline SLAs and platform cost, and leading root-cause analysis for data incidents.
Serve as the primary technical contact for client stakeholders and executives; lead design sessions and technical status reporting; surface risks, blockers, and scope changes early with proposed resolutions.
Set technical direction for data and analytics engineers, run design and code reviews, and develop teammates through pairing and structured feedback.
Contribute reusable accelerators, patterns, and enablement content to the practice, and support solutioning and estimation for new work.
Use AI-assisted engineering tooling in design and delivery and set standards for its use within the team.