• Projetar, desenvolver e manter pipelines de dados de ponta a ponta para ingestão, transformação e integração de dados. • Construir soluções ETL/ELT escaláveis e confiáveis utilizando tecnologias modernas em nuvem e ambientes on-premises. • Desenvolver e otimizar modelos de dados, data warehouses e soluções de relatórios para suportar análises de negócio. • Implementar boas práticas de governança, segurança e qualidade de dados em todas as plataformas de dados. • Colaborar com stakeholders de negócio, analistas e equipes técnicas para levantar requisitos e entregar soluções orientadas por dados. • Monitorar, solucionar problemas e aprimorar o desempenho, a confiabilidade e a escalabilidade das plataformas de dados. • Criar dashboards, relatórios e soluções analíticas que forneçam insights acionáveis para o negócio. • Apoiar iniciativas de migração para a nuvem e modernização dos ecossistemas de dados. • Garantir conformidade com padrões organizacionais, requisitos de privacidade de dados e políticas de segurança. • Orientar membros juniores da equipe e contribuir para a melhoria contínua das práticas de engenharia de dados.

Role Description As a Data Engineer / Analytics Engineer – People Analytics, you’ll play a critical role in designing and building the People Data Hub that enables trusted HR reporting today and prepares Omnicell for Workday, AI‑enabled analytics, and future HR system integrations. This is a high‑impact role for an engineer who enjoys building durable data platforms, reducing operational risk, and enabling analytics at scale. What You’ll Do Purpose: - Build and maintain the core data foundation that enables secure, governed, and scalable People analytics across Omnicell. As a Data Engineer / Analytics Engineer, you will: Data Engineering & Ingestion - Design, build, and maintain automated ingestion pipelines from HR and People systems using APIs, databases, and file‑based sources. - Ingest and transform data using modern platforms such as Microsoft Fabric, Databricks, and SQL‑based environments. - Monitor data pipelines and proactively resolve refresh failures, schema changes, and upstream data quality issues. - Implement reusable, scalable transformation patterns that minimize report‑level logic and improve long‑term reliability. Analytics Engineering & Data Modeling - Build and maintain analytics‑ready data models (facts, dimensions, and semantic layers) aligned to defined standards. - Centralize metric definitions and business logic to ensure consistent, trusted reporting across the organization. - Create, manage, and optimize certified Power BI datasets for reuse by Reporting Analysts and business partners. - Optimize models for performance, scalability, and downstream analytics consumption. Power BI Enablement (Scoped) - Support Power BI primarily at the dataset and data‑model level, not pixel‑level report design. - Define standardized measures and KPI logic to enable governed self‑service analytics. - Build or refine foundational dashboards when needed to validate data models or support adoption. Collaboration & Governance - Partner closely with the People Analytics Lead on architecture, standards, and prioritization. - Enable Reporting Analysts with clean, reliable, and well‑documented datasets. - Align data engineering work with HRIS, IT, and Workday readiness initiatives, ensuring security, privacy, and scalability. Qualifications - Minimum 3 years of experience building and supporting production‑grade data pipelines and transformations. - Strong SQL expertise and experience working with relational and analytical data models. - Hands‑on experience with Databricks, including ingestion, transformations, notebooks, and Delta Lake concepts. - Experience working in modern data platforms such as Microsoft Fabric, data lakes, or cloud analytics environments. - Proven ability to design analytics‑ready data models (facts, dimensions, semantic layers). - Experience supporting Power BI through dataset development, measure definition, and performance optimization. - Experience working with sensitive or regulated data (HR, financial, or similar), including role‑based access and privacy controls. - Strong documentation skills for data models, pipelines, and assumptions. Preferred Qualifications - Experience working with HR, People, or workforce data domains. - Exposure to REST API‑based integrations (e.g., Workday, Oracle, or similar systems). - Familiarity with AI‑enabled analytics concepts (e.g., natural‑language querying or Copilot‑style tools), without direct model development responsibility. How You’ll Elevate at Omnicell - Collaborate by partnering with People Analytics, HRIS, IT, and Reporting Analysts to deliver reusable, trusted data assets. - Inspire confidence in People data by engineering reliable pipelines and consistent metrics that leaders can trust. - Develop by continuously improving data models, platform patterns, and documentation that scale beyond individual ownership. - Execute with discipline—monitoring pipelines, resolving issues quickly, and delivering durable solutions. - Impact the organization by enabling governed analytics today and laying the groundwork for AI‑enabled insights tomorrow. Role Scope & Guardrails This role is not responsible for: - Data science or machine learning model development. - Predictive analytics ownership. - Ongoing ad‑hoc dashboard requests or executive storytelling. - Pixel‑level Power BI report design. Company Description Since 1992, Omnicell has been committed to transforming pharmacy care through outcomes-centric innovation designed to optimize clinical and business outcomes across all settings of care. We strive to be the healthcare provider’s most trusted partner by our guiding promise of “Outcomes. Defined and Delivered.” - Our comprehensive portfolio of robotics, smart devices, intelligent software, and expert services is helping healthcare facilities worldwide to improve business and clinical outcomes as they move closer to the industry vision of the Autonomous Pharmacy. - We are deeply committed to Environmental, Social, and Governance (ESG) initiatives, focusing on creating an inclusive culture and a healthier world.

Role Description En Coforge estamos buscando un Mid Data Modeler (#21797) con el siguiente perfil. - Realizar modelamiento de datos dimensional, analítico y tabular en Erwin para soluciones de Data Lake y Lakehouse. - Diseñar modelos de datos en capas para arquitecturas Big Data (UDV, DDV) y flujos hacia el Data Lake. - Modelar estructuras de datos tácticas (SDS, DATAENTRY) en Erwin para integración de arquitectura. - Manipular y transformar datos en entornos como Oracle, Impala, Hive, Databricks y Azure. - Participar en ceremonias ágiles bajo marco Scrum y gestión de tareas en Jira (Kanban). - Colaborar en el diseño e implementación de soluciones de datos en diferentes plataformas cloud. Qualifications - 3–5 años de experiencia. - Experiencia en modelamiento de datos en entornos Big Data. - Manejo de Erwin para diseño de modelos de datos. - Experiencia trabajando con Data Lake, Lakehouse y Data Warehouse. - Conocimiento de SQL y bases de datos como Oracle, MySQL, SQL Server, MongoDB y Cosmos DB. - Experiencia con herramientas y entornos como Azure, AWS, Google Cloud, Databricks, Hive, Impala y Spark. - Participación en metodologías ágiles (Scrum) y uso de Jira. Requirements - Conocimiento de Python para manipulación de datos. - Experiencia con SSIS, SSAS, SSRS. - Conocimiento de Power BI y herramientas de visualización. - Experiencia en Power Automate y automatización de procesos. - Conocimiento de sistemas operativos Linux, Windows y Android. Company Description En Coforge, contratamos profesionales únicamente en función de sus habilidades y calificaciones, y no discriminamos en función de la edad, discapacidad, religión, género, orientación sexual, estado socioeconómico o nacionalidad.

• Supervise junior members of the data engineering team. • Guiding, planning, and reviewing the team's work • Create and maintain optimal data pipeline architecture • Assemble large, complex data sets that meet functional / non-functional business requirements • Extend our machine learning platform by designing tools that interface with cloud services, our current code base, and provide new flexibility in model building • Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL, Python, and AWS • Build analytics tools to provide actionable insights into key business performance metrics, as well as supporting the needs of the analytics team • Create data-handling tools for analytics and data scientist team members that assist them in building and optimizing our decision-making process