Top Tech Jobs & Startup Jobs

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.

Build and maintain dbt models in a production Snowflake data platform, implement entity resolution and AI-powered tagging, write data quality tests, maintain source documentation and lookup tables, monitor dbt Cloud jobs, debug data issues, and submit documented pull requests. The role requires strong SQL, dbt, cloud data warehouse, dimensional modeling, Git workflows, and remote collaboration skills.

Build and deploy production-grade agentic AI solutions across Palantir Foundry, Azure, and Databricks. Responsibilities include designing modular agent skills, extending enterprise ontologies, engineering data pipelines, integrating and optimizing LLMs, developing CI/CD pipelines, and delivering use cases from experimentation through production while collaborating directly with clients.

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.