Jobs at Entrada

Architect and lead implementation of an enterprise Generative AI Lakehouse platform on Databricks. Responsibilities include building document ingestion and structured extraction pipelines, enforcing anti-hallucination guardrails, developing Human-in-the-Loop review applications, integrating ERP feeds, and establishing governance, security, lineage, and auditability. The role also involves client consultation, architectural documentation, mentoring, and translating complex GenAI solutions for technical and executive stakeholders.

Design and scale Databricks Genie conversational data interfaces, including Genie Spaces, semantic layers, SQL guidance, testing, fine-tuning, and external integrations. Build optimized Python, SQL, and PySpark pipelines in Delta Lake, support LLM-based metadata enrichment and semantic optimization, and translate business requirements into reliable data models. Collaborate with stakeholders and maintain high standards for accuracy, performance, and governed enterprise data access.

Develop and execute sales strategies for consulting services, identify and qualify target accounts, manage opportunities from lead through close, and grow relationships with direct clients and technology partners. The role involves pipeline management, prospecting, targeted messaging, partner collaboration, market-facing activities, and representing Entrada at client meetings and industry events. Candidates should build credibility in data and analytics and use HubSpot, LinkedIn, AI, and other prospecting tools.

Design, deploy, and optimize machine learning models and infrastructure using Databricks. Lead end-to-end model development, including data preparation, deployment, monitoring, and production scaling. Collaborate with data scientists, engineers, business stakeholders, and clients to deliver technical solutions. Mentor junior team members and apply advances in machine learning, NLP, computer vision, and MLOps to solve complex business problems.

Design and lead scalable data architectures for client organizations, covering data lakes, warehouses, databases, integrations, governance, security, and quality. Translate business requirements into architecture solutions, optimize cloud and on-premise systems for performance and cost, guide engineering teams, document designs, and maintain expertise in Databricks and modern data technologies. The role requires up to 20% travel to client sites.

Lead the architecture and development of scalable Databricks data solutions for global clients. Build PySpark pipelines, migrate legacy warehouses to Delta Lake, optimize Spark performance and costs, implement governance with Unity Catalog, and establish code quality and CI/CD standards. Advise stakeholders on data strategy, mentor engineers, and deliver modern Lakehouse architectures using cloud platforms and contemporary orchestration tools.

Designs and oversees enterprise data architectures spanning databases, warehouses, lakes, integrations, and cloud environments. Translates business requirements into scalable, secure, and cost-efficient solutions; establishes data modeling, governance, quality, metadata, and security practices; guides engineers and analysts; optimizes infrastructure; and maintains architecture documentation. Requires extensive Databricks, data engineering, big data, cloud, and stakeholder leadership experience.