Managing Consultant, Data Architect

**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. Hakkoda, an IBM company, is a modern data and AI consultancy. Our mission is to push the boundaries of data and AI, to be the partner clients trust for their most complex data challenges, and to create the kind of company that people want to be a part of. We bring deep expertise to Snowflake and the broader modern data stack, we are easy to work with, and we turn repeat problems into reusable solutions. Our team is distributed across North America, Latin America, and Europe, and we hire curious people who want to learn, experiment, and lead. **Your role and responsibilities** We are looking for a Managing Consultant, Data Architect to join our growing team of experts. This position is the principal technical authority for enterprise data and AI platforms across a portfolio of client engagements, with Snowflake and Databricks and cloud-native services on AWS, Azure, and GCP alongside it. The work spans target-state architecture and roadmaps, data modeling and integration patterns, security and governance architecture, and the reference architectures and standards that keep delivery consistent across engagements. The ideal candidate leads architecture through delivery rather than handing off a design. They define the target state, decompose it into workstreams, make and document the trade-offs, and review what engagement teams build against it. They have been the lead architect on multiple enterprise platform implementations, lead discovery and executive advisory with client leadership, and bring a business-first lens to every design decision. They incorporate AI tooling into how they design and deliver, advise clients on AI readiness and where AI fits in their data strategy, and architect platforms that support AI workloads while keeping foundational data quality, structure, and governance intact. This role carries principal-level ownership and a very high bar for delivery. The Managing Consultant, Data Architect is accountable for the technical outcome across every engagement they govern: the architecture is sound, the standards are followed, what ships to the client meets the design, and client executive sponsors trust them to make and defend the hard technical calls. They set technical direction for engagement architects and lead engineers, run architecture reviews, author the practice's reference architectures, lead solutioning and pre-sales for new work, and grow the practice's architecture bench through mentorship. They lead through technical authority rather than line management and thrive in a consulting environment where no two engagements are the same. This role can be performed from anywhere in the US. Responsibilities: • Target-State Architecture: Own end-to-end target-state architecture for enterprise data and AI platforms across concurrent engagements, including platform topology, environment strategy, security and governance model, and cost and performance architecture at advanced-certification depth. • Roadmaps and Executive Advisory: Lead architecture assessments and AI readiness and data maturity evaluations; translate findings into phased roadmaps with success metrics, investment cases, and cost guardrails; present and defend them to client executives. • Data Modeling and Integration Patterns: Design layered data models (medallion, dimensional, Data Vault, 3NF) and semantic layers; select and justify integration patterns (batch, CDC, event-driven, API, data sharing) and record the trade-offs as architecture decision records. • Reference Architectures and Standards: Author and maintain the practice's reference architectures, decision records, and reusable patterns for ingestion, transformation, orchestration, observability, governance, and AI-ready data; define engineering standards for transformation frameworks, CI/CD, and infrastructure as code. • Architecture Governance: Run architecture reviews for engagement architects and lead engineers; approve or redirect designs before build; resolve cross-engagement design questions. • Platform Direction: Set adoption direction for platform-native and AI capabilities (for example Snowflake Cortex, Snowpark, Iceberg and open table formats, Databricks Unity Catalog and Mosaic AI) and define where each platform fits in a client's estate, including coexistence and migration architectures. • Delivery Accountability: Hold the technical outcome through delivery: decompose the architecture into workstreams, review what ships against the approved design, surface architectural risk, scope drift, and technical debt early with proposed resolutions, and partner with project and practice leadership on delivery health. • Solutioning and Pre-Sales: Own architecture, effort estimates, staffing shapes, and technical narrative for proposals and statements of work; lead technical discovery and solution design in pursuit cycles alongside sales and practice leadership. • Technical Leadership and Mentorship: Set technical direction for engagement teams; mentor architects, data engineers, and analytics engineers; support hiring, onboarding, and technical enablement for the practice. • Practice and Partner Contribution: Contribute accelerators, estimation models, and enablement content; represent the practice with platform partners and in technical communities. • AI-Assisted Delivery: Use AI tooling in design and delivery work and set standards for its use across engagement teams. This job can be performed from anywhere in the US. **Required technical and professional expertise** • 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 hands-on at advanced depth on at least one modern cloud data platform: Snowflake (account and warehouse topology, clustering and performance, RBAC and security model, cost governance, Streams, Tasks, Dynamic Tables, Snowpark, data sharing, Iceberg, Cortex) or Databricks (Delta Lake, Unity Catalog, Spark, Workflows, Delta Live Tables, Mosaic AI), with working knowledge of the other sufficient to design coexistence or migration architectures. • Deep expertise in enterprise data modeling (dimensional, Data Vault, 3NF) and target-state platform design across ingestion, transformation, semantic, and consumption layers, including data mesh and data product patterns. • Proven track record designing batch and streaming ingestion frameworks and choosing among integration patterns (CDC, event-driven, file-based, API) based on client constraints. • Strong proficiency in SQL and Python; hands-on with ELT and orchestration tooling such as dbt, Matillion, Informatica, Airflow, or platform-native pipelines, sufficient to review, prototype, and unblock delivery work. • CI/CD and infrastructure as code for data platforms (GitLab, GitHub Actions, or Azure DevOps; Terraform): pipelines, environment promotion, and automated testing. • Cloud platform experience on AWS, Azure, or GCP, including networking, IAM, storage, private connectivity, and cost considerations for data platforms. • Experience architecting platforms that support 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: setting technical direction, conducting architecture, design, and code reviews, and mentoring architects and engineers. • Track record of owning technical delivery across complex, multi-workstream engagements, including estimation and risk management. • Track record contributing architecture, estimates, and technical narrative 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. • Comfortable working in Agile delivery environments. **Preferred technical and professional experience** • Platform Certifications: SnowPro Advanced: Architect; Databricks Certified Data Engineer Professional or Solutions Architect credential. Holding both is a strong plus. • Cloud Architecture Certification: AWS Solutions Architect Professional, Azure Solutions Architect Expert, or GCP Professional Data Engineer. • Platform Migration Leadership: Leading migrations from legacy warehouses (Teradata, Oracle, SQL Server, Hadoop) to modern cloud platforms, including assessment, wave planning, and validation. • Data Governance and Security Architecture: Designing governance frameworks, data quality controls, lineage, and cataloging across enterprise platforms (Alation, Atlan, Collibra, or equivalent). • Streaming and Event-Driven Architecture: Kafka, Kinesis, EventBridge, or Event Hubs for operational and analytic data movement. • Applied AI in Production: MLOps practices, model serving, agentic data workflows, and operating AI workloads on data platforms in production environments. • FinOps for Data Platforms: Cost governance models, chargeback, and optimization programs across platform and cloud spend. • Industry Context: Regulated industry experience (financial services, insurance, healthcare and life sciences, energy, public sector). • Thought Leadership: Contributions to technical communities through speaking, writing, open source, or internal enablement. 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.