Consulting Partner - Data & Analytics

ClifyX; Santa Clara (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

ClifyX Inc. is seeking a Consulting Partner - Data & Analytics to lead advisory, solutioning and program execution for high-tech clients on the US West Coast. The role spans architecture, assets, and end-to-end delivery across the data agenda.

You will partner with client teams, run discovery and executive briefings, and contribute to RFP/RFI responses. Travel to customer sites is expected as part of delivering value.

Qualifications

• Experience advising and delivering data & analytics programs for high-tech customers.
• Ability to plan and run discovery conversations and executive briefings.
• Proven track record in solutioning, architecture design and proposal responses.
• Experience shaping assets, accelerators, PoVs, demos and reference architectures.
• Willingness to travel regularly to customer sites across the US West Coast.

Responsibilities

• Engage with customers to build trusted relationships and advise on data strategy.
• Lead workshops to frame problems and design target architectures.
• Own or contribute to RFP/RFI responses with architecture and pricing inputs.
• Identify reusable assets and drive accelerators and PoCs to adoption.
• Represent the Data & AI CoEs to customers and shape the CoE roadmap.

Skills

Data architecture

Cloud data platforms

Spark / PySpark

SQL & Python

Looker / Power BI / Tableau

Data governance

GenAI awareness

Client engagement

Tools

Airflow

Terraform

Kubernetes

GitHub Actions

Databricks

Snowflake

Job description

Job Title: Consulting Partner - Data & Analytics

Job Type: Full Time

Job Description:

We are seeking a consulting partner to be the on-the-ground face of the Data & Analytics unit serving high-tech customers on the US West Coast. This is a broad, hands-on, customer-facing role spanning advisory, solutioning, asset building and program execution - suited to someone who enjoys operating across the full breadth of the data agenda rather than a single specialism.

Key Responsibilities

• Customer engagement and advisory: Build and sustain trusted relationships with client data engineering, platform, architecture and analytics teams. Travel to customer sites across the US West Coast to run discovery conversations, executive briefings and capability sessions.


• Solution workshops: Plan and facilitate solution and design workshops - frame the problem, shape options, whiteboard target architectures and convert outcomes into actionable proposals.


• Solutioning and proposals: Own or contribute to RFP, RFI and proactive proposal responses - solution approach, architecture, estimation, delivery model and pricing inputs - and present and defend them to technical and business audiences.


• Asset and offering planning: Identify reusable asset opportunities from live customer demand; define accelerators, PoVs, demos and reference architectures, and drive them to completion with the CoE pods.


• Representing the Centres of Excellence: Act as the customer-facing voice of the Data and AI CoEs - bring the right pod, offering and expertise into the account, and carry customer insight back to shape the CoE roadmap.


• Program planning and execution: Work with offshore delivery and CoE teams on program planning, estimation, staffing and execution governance for key programs; guide design decisions, review deliverables and safeguard quality and outcomes.


• Technology currency and capability building: Maintain an informed point of view on modern data architecture, open-source engineering and AI trends relevant to high-tech customers; contribute to skilling priorities and mentor engineers and consultants.


• Pipeline contribution: Identify and qualify opportunities within accounts, maintain a credible view of technical pipeline and work with account leadership to convert.

Must-Have Technical / Functional Skills

Data & Analytics Platform Engineering - Core of the Role

• Modern warehousing, lakehouse and open data architecture: Semantic data modelling, medallion architecture, Apache Iceberg / Delta, Trino / Presto, dbt, and Data Mesh / data product design for reusable, governed datasets.


• Cloud data and analytics platforms - deep expertise in at least one, credible familiarity with a second: Google Cloud (BigQuery, Dataflow, Dataproc, Pub/Sub, Dataplex, Looker, Vertex AI), Azure (Synapse, Fabric, ADF, Purview, Azure ML) or AWS (Redshift, Glue, EMR, Kinesis, SageMaker); Databricks and/or Snowflake, including workload design and query performance tuning.


• Deep, hands-on data engineering at scale: Enterprise data pipelines using Apache Spark / PySpark, advanced SQL and Python; batch and streaming ingestion with Kafka and/or Flink; orchestration and SLA-driven operations with Airflow.


• Analytics and consumption enablement: The semantic and reporting layer that turns data products into decisions - Looker / LookML, Power BI or Tableau - with self-service enablement and analytics engineering practices.


• DataOps and platform reliability: CI/CD for data pipelines (GitHub Actions / Azure DevOps), Terraform, Kubernetes, data observability and lineage, and FinOps for cost and performance optimisation of large data platforms.

Data Governance, AI and Analytics Breadth

• Data governance, quality, lineage, metadata, MDM and privacy engineering: Catalog tooling such as Dataplex, Collibra, Alation, Purview or Unity Catalog.


• AI & GenAI led Transformation experience.


• Working knowledge of the AI stack: ML and MLOps (MLflow, Kubeflow, Ray, feature stores), GenAI and agentic patterns (RAG, vector stores, LLM evaluation and guardrails) and Responsible AI practices.

Consulting, Solutioning and Engagement Skills

• Proven experience in a customer-facing role - running discovery and solution workshops, presenting to senior engineering and business stakeholders, and building trusted technical relationships.


• Demonstrated ownership of RFP / RFI and proactive proposal responses: solution approach, architecture, estimation, delivery model and orals.


• Experience building reusable assets - Accelerators, PoVs, PoCs, demos and reference architectures - and representing a practice or Centre of Excellence.


• Experience in planning and governing programs executed with distributed onsite-offshore teams.

Domain Context

• Experience with high-technology customers - hyperscalers, platform, software, semiconductor, consumer-electronics and comfort working alongside engineering-led organisations that build much of their own stack.

Generic Managerial Skills

• Strong consultative and communication skills - credible with principal engineers and confident presenting to executive stakeholders.


• Willingness and ability 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 without direct line management.


• Comfortable working in evolving environments - self-directed, pragmatic and able to build structure where needed.