• Definir y validar arquitecturas E2E (frontend, middleware, backend) en entornos distribuidos. • Diseñar y evolucionar soluciones de integración y APIs (REST & Async) con Mulesoft. • Liderar equipos y actuar como referente técnico en la toma de decisiones. • Coordinar stakeholders y asegurar buenas prácticas, calidad y estándares. • Supervisar la implementación y detectar riesgos técnicos proponiendo soluciones.

Role Description Highly specialized Principal Scientific Data Architect to bridge the gap between advanced Google Cloud engineering and life sciences discovery. This role will redefine how scientific data is structured, scaled, and consumed across our R&D, Onyx, and CMC (Chemistry, Manufacturing, and Controls) divisions. Operating natively within the Google Cloud Platform (GCP) and Databricks on GCP ecosystem, will lead the transition toward a fully automated, software-defined data framework by implementing: - Schema as Code - Data as Code - Metadata-driven Configuration Data Engineering The ideal candidate combines elite cloud data architecture expertise with deep scientific literacy, enabling the design of data systems that directly power in-silico molecular discovery and autonomous Agentic AI frameworks. Key Responsibilities - GCP-Native Data Architecture & Paradigm Shifts - Schema as Code: Design and implement version-controlled, programmatically managed data schemas natively integrated with Google BigQuery. Ensure schemas evolve seamlessly using GCP DevOps tools (Cloud Build, Artifact Registry) and Terraform. - Data as Code: Treat data assets with software engineering rigor. Implement data versioning, programmability, and automated quality testing using BigQuery features (like Table Snapshots and Time Travel), dbt, and Delta Lake on GCP. - Configuration Data Engineering: Architect highly optimized, metadata-driven, configuration-led data pipelines using Google Cloud Composer (Airflow) or Dataflow to abstract infrastructure complexity. - Scientific Domain Integration - Translate complex biological and chemical concepts (e.g., molecular modalities, chemical structures, solubility traits) into highly scalable logical and physical data models within BigQuery and Databricks. - Collaborate closely with computational chemists, biologists, and AI engineers to ensure the data architecture natively supports predictive in-silico modeling. - Design robust data layouts that allow autonomous AI agents to easily "dip into" molecular data, extract properties, and explain molecular behavior. - Platform & Ecosystem Strategy - Optimize the interoperability between Databricks on GCP (Lakehouse architecture) and enterprise-wide Google BigQuery storage and analytics. - Inform the integration of semantic web technologies and knowledge graphs (e.g., StarDog) into the overarching Google Cloud data fabric. - Ensure data availability and high-performance querying for downstream multi-agent AI ecosystems (Agentic Hubs built on Google Cloud's AI suite or custom frameworks). Qualifications - Scientific Domain Knowledge: Strong background or proven experience working inside life sciences, pharmaceuticals, biotech, or scientific research organizations. - Ability to converse fluently with scientists regarding therapeutic modalities, molecular properties, and R&D pipelines without needing to be a wet-lab scientist. - GCP & Technical Architecture Expertise: - Mastery of Google BigQuery (including BigLake, analytics hubs, and nested JSON schemas) and Databricks on GCP. - Proven track record of implementing Schema as Code and Data as Code paradigms using tools like Terraform, dbt, and Git-based CI/CD workflows. - Deep experience with configuration-driven pipeline orchestrators, specifically Google Cloud Composer / Apache Airflow. - Strong understanding of relational, dimensional, and graph-based data modeling. Familiarity with knowledge graphs (e.g., StarDog) or biomedical ontologies is a major plus. - Soft Skills & Leadership: - Ability to conceptualize and suggest complex in-silico data solutions at a high strategic level without getting bogged down by immediate technology limitations. - Exceptional ability to articulate the business and scientific value of pure data architecture to non-technical executive stakeholders. Preferred Qualifications - Professional Google Cloud Data Engineer or Google Cloud Professional Cloud Architect certification. - Degree in Computer Science, Data Engineering, Bioinformatics, Computational Chemistry, or a related quantitative field. - Experience setting up GCP data foundations specifically engineered to feed Large Language Models (e.g., Vertex AI / Gemini) and autonomous AI agents.

• Prepares complete, accurate and code compliant construction documents • Performs assignments under direction and supervisory approval of others for all projects • Provides technical and creative design solutions that align with program intent • Applies clients program requirements and design criteria to existing buildings and structures • Responsible for determining the appropriate building components and their design application • Responsible for maintaining project schedules, deadlines and commitments for small and medium-scope assignments of average difficulty • Notifies supervisor of changes in project scope and advises if a change order proposal is needed • Coordinates work with consultants, contractors, fabricators, specification writers, and regulatory agencies to meet overall project objectives • Possesses a working knowledge of building codes and ADA requirements • Conducts project due diligence with municipalities to determine local zoning and building code requirements • Accountable for resolving plan review comments from code officials • Assists the project liaison to code officials, outside consultants, suppliers, and contractors regarding questions to the construction documents • Adheres to CAD standards and follows assigned drafting protocol across all projects • Receives feedback and direction from others • Works with supervisor and peers to pursue professional development • Participates in NCARBs Architectural Experience Program (AXP) Program • Accountable for timely completion of assigned projects • Self-starter with the ability to work independently on simple to moderately difficult projects • Capable to prioritize and work on multiple projects at the same time • Other duties as assigned

• Support the implementation of Parloa's AI solutions, enhancing customer experiences. • Craft effective AI workflows and build connections to enterprise systems. • Design, prototype, and validate conversational solutions for AI agent deployments. • Guide customers and partners in maximizing the value of Parloa's AI solutions. • Scope and implement AI Agent deployments, providing strategic advice and execution support to customers and partners. • Collaborate with Forward Deployed Engineers on complex integration scenarios.