Job Description
We are seeking a Data and AI Consultant for a newly formed Data & Analytics unit serving high-technology customers on the US
West Coast. This is a senior, customer-facing role that combines advisory and solutioning leadership with practical knowledge
across Data Engineering, Data Management, conversational analytics, advanced analytics and AI. The consultant will shape
account-level Data and AI agendas, identify opportunities, develop roadmaps and work with CoE and delivery teams to take
solutions into production.
Key Responsibilities
" Data and AI advisory: Build trusted relationships with customer data, analytics, AI and platform leaders; assess business and
technology priorities; and define practical transformation roadmaps.
" Data Engineering and platform solutioning: Shape modern data architectures and engineering solutions covering ingestion,
transformation, lakehouse / warehouse, streaming, integration and data serving.
" Data Management: Advise on governance, metadata, cataloging, quality, lineage, Master Data Management, security and privacy
to make enterprise data trusted and AI-ready.
" Conversational analytics: Identify and solution natural-language analytics opportunities such as text-to-SQL, chat-based BI,
semantic search, RAG-enabled insight assistants and conversational workspaces, with appropriate governance and access
controls.
" AI and GenAI solutioning: Lead discovery and design for ML, GenAI and agentic use cases; assess feasibility and value; define
target architecture; and establish evaluation, monitoring and Responsible AI guardrails.
" Customer workshops: Plan and facilitate Data and AI discovery sessions, current-state assessments, architecture workshops and
use-case prioritization discussions, converting outcomes into a sequenced roadmap and qualified pipeline.
" Proposals and presales: Own or contribute to RFP, RFI and proactive proposals, including solution approach, reference
architecture, estimates, delivery model, staffing and pricing inputs; present and defend recommendations to business and
technical stakeholders.
" Offerings and assets: Convert recurring customer needs into reusable accelerators, PoVs, demos, reference architectures and
playbooks with Data and AI CoEs and alliance partners.
" Delivery governance: Guide programs from assessment and PoC through pilot and production; review key architecture and
engineering deliverables; and ensure quality, security, observability, cost control and business outcomes.
" Capability building: Define skill priorities across Data Engineering, Data Management, analytics and AI; mentor consultants,
architects and engineers; and maintain a current market and technology point of view.
Qualifications
Experience: Demonstrated senior-level experience in Data and AI consulting, solution architecture, and customer-facing technology
leadership.
Education: Bachelor's degree in Computer Science, Engineering, or a related discipline, or equivalent relevant professional
experience; Master's or PhD desirable.
Must-Have Technical / Functional Skills
Data, AI and conversational analytics - core of the role
" Strong consulting and solution architecture experience spanning modern data platforms, Data Engineering, Data Management,
analytics, Generative AI and conversational analytics.
" Working knowledge of Data Engineering using Python, SQL, Spark / PySpark, ETL / ELT, data pipelines, batch and streaming
patterns, APIs, Kafka and cloud-native orchestration services.
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Data and AI Consultant | Publication-ready JD
" Working knowledge of Data Management, including data architecture, lakehouse and medallion patterns, Data Mesh, Master
Data Management, metadata, cataloging, data quality, lineage, security, privacy and governance.
" Hands-on understanding of conversational analytics and natural-language access to enterprise data, including text-to-SQL,
semantic layers, RAG, enterprise search, chat-based BI, insight generation and responsible access controls.
" Deep understanding of GenAI engineering: RAG over enterprise data, prompt and context engineering, embeddings and vector
stores such as Pinecone, Weaviate or Milvus, model selection and LLM fine-tuning using LoRA / QLoRA.
" Practical experience with agentic AI patterns including planning, tool use, function calling, memory and multi-agent orchestration
using LangChain, LangGraph, Semantic Kernel or equivalent.
" Knowledge of classical ML and data science using Python, PyTorch / TensorFlow and scikit-learn, including feature engineering,
model evaluation and interpretability.
" Production awareness across DataOps, MLOps and LLMOps: CI/CD, testing, observability, model and data monitoring, drift,
cost management, Docker and Kubernetes.
Cloud data and AI platform foundation
" Strong expertise in at least one cloud data and AI stack, with familiarity in a second: Google Cloud (BigQuery, Dataflow,
Dataproc, Dataplex, Vertex AI), Azure (Data Factory, Databricks, Fabric, Purview, Azure ML, Azure OpenAI) or AWS (Glue,
EMR, Redshift, SageMaker, Bedrock).
" Experience with Databricks and/or Snowflake, including lakehouse or warehouse architecture, data engineering, governance and
AI capabilities such as Mosaic AI or Cortex.
" Ability to design an AI-ready data estate with scalable ingestion, transformation, semantic and serving layers, backed by data
quality, lineage, governance and observability.
Consulting, CoE leadership and solutioning
" Proven experience leading or building a Data and AI practice, CoE or capability pod, defining offerings and assets, setting
architecture standards, and mentoring data, analytics and AI teams.
" Strong customer-facing track record in discovery and design workshops, use-case prioritization, current-state assessment,
roadmap creation and executive presentations.
" Ownership of RFP / RFI and proactive Data and AI proposals covering solution approach, target architecture, estimation, delivery
model, commercials inputs and orals.
" Experience leading initiatives from assessment and PoC through pilot and production with distributed onsite-offshore teams.
Domain context
" Experience with high-technology customers including hyperscalers, software and platform companies, semiconductor
organizations and consumer-electronics enterprises.
Generic Managerial Skills
" Ability to lead a technical capability without formal line authority - setti ng direction, standards and priorities across pods and
accounts.
" Strong consultative and communication skills - credible with principal AI engineers and confident presenting to executive
stakeholders; willing and able to travel regularly to customer locations across the US West Coast.
" Experience coordinating across geographies and time zones with offshore delivery and CoE teams; mentoring and growing
technical talent.
" Comfortable in an early-stage unit - self-directed