Jobs at Blue Orange Digital

Act as the founders' right hand to operationalize AI: prototype internal AI workflows, turn experiments into company-wide programs, run executive cadence and OKRs, unblock cross-functional projects, prepare strategic briefs, enforce responsible AI guardrails, and drive exit-readiness and organizational health.

Own new and existing accounts, services revenue, forecasting, technical discovery, engagement scoping, pricing, proposals, and statements of work. Build architectures, demos, proofs of concept, and the first delivery sprint for sold engagements. Develop Snowflake and Databricks partner channels, support co-selling, and refine service offerings and pricing based on client feedback.

Embed with client teams to map business processes and transform them into production-grade agentic AI workflows. Build LLM agents, integrations, retrieval systems, evaluations, guardrails, observability, and human-in-the-loop processes using Python and agent frameworks. Facilitate stakeholder sessions, explain technical tradeoffs, document solutions, and hand off maintainable workflows. Bring reusable engineering patterns back to the consultancy.

Design enterprise data architectures and data models across greenfield and consolidation environments. Define non-functional requirements, ownership boundaries, and data flows using Databricks medallion architecture. Create substantive client-facing and executive-ready presentation content while advising technically sophisticated consulting clients. This senior individual-contributor role requires strong communication, architectural judgment, and experience with complex data platforms.

Build and maintain end-to-end Databricks data pipelines using bronze, silver, and gold medallion architecture. Write Python, SQL, and PySpark code, including materialized views. Investigate unfamiliar client data, communicate findings, and contribute to data modeling, governance discussions, and technical demonstrations. Work independently with significant technical ownership while collaborating directly with consulting clients.

Own the architecture and delivery of production AI and machine-learning systems for enterprise clients. Lead engineers, build cloud data pipelines, develop causal and predictive models, create mathematical optimization engines, and integrate generative or agentic AI solutions. Serve as the technical face of client engagements through executive presentations, technical reviews, governance documentation, and delivery oversight. Ensure code quality, testing, reproducibility, auditability, privacy, and responsible use in regulated environments.

Own end-to-end data engineering projects and build scalable ELT pipelines using Snowflake, dbt, and Airflow. Responsibilities include performance tuning, dimensional modeling, data warehousing, BI data preparation, platform modernization, automation, risk identification, and mentoring engineers. The role also supports future AI/ML data capabilities and requires strong independent delivery in a post-acquisition SaaS data environment.

Build production LLM agents, multi-step workflows, MCP servers, evaluation and observability systems, and document-extraction pipelines. Architect AI-ready semantic data layers over Snowflake and dbt, develop Python ingestion and Airflow workloads, and implement retrieval, embeddings, governance, security, and reliable agent operations. Lead technical design, mentor peers, and advise clients in an embedded consulting engagement.

Lead client-facing analytics engagements: translate ambiguous business questions into data models, dashboards, and KPIs; write production-quality SQL and dbt models; build BI reporting; apply AI tools to accelerate analysis; drive proactive insights; set stakeholder cadence; and mentor junior analysts to ensure adoption and delivery.

Design and implement an enterprise data governance framework for a Microsoft Fabric data warehouse spanning ERP, CTRM, SAP, trading, finance, risk, and logistics data. Establish cataloging, lineage, ownership, access controls, data quality rules, naming and classification standards, master data management, stewardship processes, and governance documentation. Collaborate with integration workstreams and train client data owners and analysts in a part-time advisory engagement.

Seller-doer solution architect who leads technical discovery, architects multi-vendor data and AI platforms, builds proposals and carries quota, then stays hands-on to guide delivery, write production code, and expand accounts across Snowflake, Databricks, cloud, and modern data tooling.

Lead Databricks pre-sales discovery and design Lakehouse architectures, author proposal technical content, run proofs-of-concept, and act as lead architect on major engagements. Own the Databricks partner relationship and co-sell motion, drive certification and internal enablement, resolve performance and governance challenges, and ensure successful production handoffs while building partner-sourced pipeline and scaling the Databricks practice.

Lead hands-on design and delivery of production-grade AI solutions within a vertical. Architect LLM and agentic systems, cloud and data platforms, run technical deep-dives, advise executives, support pre-sales, and build reusable reference architectures and domain-specific accelerators to scale engagements and practice growth.

Lead and grow the private equity channel through direct selling, pipeline creation, and relationship activation. Carry quota, close consultative PE-focused engagements ($150K–$1M+), define the PE sales playbook, represent the firm at industry events, and coordinate with delivery teams to ensure smooth handoffs and successful implementations.