<p>We are looking for a Contract Data Engineer to support financial systems and data operations in San Francisco, California. This is a remote on-going contract. This role will work across Finance, Systems, and Engineering to improve data reliability, strengthen integrations, and enhance platforms that support billing, accounts payable, and quote-to-cash workflows. The ideal candidate brings deep technical expertise with NetSuite and Snowflake, along with strong coding skills to diagnose issues, streamline processes, and build scalable solutions in a complex systems environment.</p><p><br></p><p>Responsibilities:</p><p>• Build, monitor, and refine data pipelines that move financial and transactional information across internal platforms and enterprise systems.</p><p>• Investigate data issues, resolve integration failures, and improve the overall accuracy and consistency of business-critical datasets.</p><p>• Develop and maintain middleware solutions that connect NetSuite, Salesforce, Snowflake, and custom applications.</p><p>• Partner with Finance, Systems, and Engineering stakeholders to deliver technical enhancements that support accounting operations and downstream reporting.</p><p>• Implement improvements within NetSuite and related financial systems to better support billing, accounts payable, and quote-to-cash processes.</p><p>• Troubleshoot code and system behaviors using SQL, Python, and related tools to identify root causes and restore reliable performance.</p><p>• Support integration workflows between Salesforce and NetSuite, ensuring stable data exchange and operational continuity.</p><p>• Evaluate and contribute to automation initiatives, including AI-enabled workflow opportunities that can reduce manual effort and improve efficiency.</p>
<p>Our client is seeking a Contract Data Analyst to support a Salesforce modernization project in San Mateo, CA. This hybrid role (1-2 days onsite) will focus heavily on data cleanup, data integrity, and overall data quality as the organization works to enhance and streamline its Salesforce environment. The ideal candidate will have strong experience analyzing, cleansing, and validating large datasets, with a particular emphasis on CRM data and process improvement. This person will partner with business and technical teams to identify data issues, standardize records, and help ensure the success of the Salesforce transformation initiative. </p><p><br></p><p><strong>Key Responsibilities:</strong></p><ul><li>Perform data cleanup, deduplication, normalization, and validation within Salesforce and related systems. </li><li>Identify data quality issues, inconsistencies, and gaps, and recommend corrective actions. </li><li>Support data migration, mapping, and integrity checks tied to the Salesforce modernization effort. </li><li>Analyze existing data structures and help establish best practices for ongoing data governance and stewardship.</li><li>Create reports, dashboards, and ad hoc analyses to support project stakeholders and business decision-making. </li><li>Collaborate with cross-functional teams, including business users, IT, and project leadership, to ensure data accuracy and consistency. </li><li>Document data definitions, business rules, and cleanup processes. </li><li>Assist with testing and validation of updated Salesforce workflows, fields, and records. </li></ul><p><br></p>
<p>Data Analyst</p><p><strong>Job Description</strong></p><p>Seeking a Data Analyst to collect, analyze, and interpret data to help drive business decisions and improve operational performance.</p><p><strong>Responsibilities</strong></p><ul><li>Analyze large datasets and identify trends</li><li>Create reports, dashboards, and visualizations</li><li>Perform data validation and quality checks</li><li>Present findings and recommendations</li><li>Collaborate with cross-functional teams</li></ul>
<p>Our client is seeking a Data Scientist II – Generative AI to join a cutting-edge team focused on building scalable, production-ready AI solutions that transform business workflows and deliver measurable impact across global operations. This role is ideal for professionals passionate about leveraging Generative AI technologies, creating intelligent agents, and driving innovation at scale.</p><p><br></p><p>You will design and implement GenAI-powered agents that streamline internal processes, enhance productivity, and support business development initiatives. Responsibilities include developing robust prompt engineering frameworks, building RAG pipelines, and converting prototypes into production-ready solutions. You’ll collaborate closely with engineering and business teams to ensure solutions meet diverse client needs and are optimized for global deployment.</p><p><br></p><p>Key projects include extending the company’s GPT platform, creating AI agents that improve efficiencies for RFP development, onboarding materials, and SOW requirements. Success in this role means quickly ramping up on backlog projects, delivering high-priority initiatives, and staying ahead of emerging GenAI frameworks to continuously advance internal AI capabilities.</p>
<p><strong>Data & Knowledge Engineer</strong></p><p><br></p><p><strong>Company Overview</strong></p><p>A leading artificial intelligence and advanced analytics organization is seeking a Data & Knowledge Engineer to help power next-generation AI and decision-support platforms. Based in Los Angeles, California, the company specializes in integrating complex data from disparate sources into unified intelligence systems that support advanced analytics, automation, and operational decision-making. This is an opportunity to work on mission-critical initiatives involving large-scale data, knowledge graphs, and AI-driven applications.</p><p><br></p><p><strong>Role Summary</strong></p><p>The Data & Knowledge Engineer will lead the onboarding, transformation, and governance of complex multimodal data into scalable data and knowledge platforms. This role focuses on integrating structured and unstructured data sources, designing reusable data pipelines, developing entity resolution frameworks, and enabling high-quality data for analytics, retrieval, AI workflows, and geospatial applications. The ideal candidate combines strong data engineering expertise with experience in knowledge graphs, data quality, and large-scale information management.</p><p><br></p><p><strong>Key Responsibilities</strong></p><ul><li>Design and develop scalable data ingestion pipelines for structured, unstructured, geospatial, and sensor-based data sources.</li><li>Build and maintain batch and streaming data processing systems across cloud, on-premises, and disconnected environments.</li><li>Develop integrations with APIs, databases, file systems, enterprise applications, and external data sources.</li><li>Design schema mapping, normalization, and transformation processes that support diverse customer data models.</li><li>Implement entity resolution, record linkage, deduplication, and data matching capabilities across multiple sources.</li><li>Preserve data lineage, provenance, auditing, and traceability throughout the data lifecycle.</li><li>Create data validation, monitoring, replay, and exception-handling processes for complex data environments.</li><li>Develop workflows for managing ambiguous records, conflicting information, and data quality issues.</li><li>Define and measure data quality metrics, onboarding effectiveness, and operational performance indicators.</li><li>Support knowledge graph, retrieval, AI, and analytics capabilities through high-quality governed datasets.</li><li>Partner with engineering and stakeholder teams to transform recurring onboarding requirements into reusable platform capabilities.</li><li>Contribute to platform architecture, engineering standards, and long-term data strategy initiatives.</li></ul><p><strong>Additional Details</strong></p><ul><li>Fully onsite 5 days a week</li><li>Full-time exempt position</li><li>Hands-on engineering role with substantial ownership and technical influence</li><li>Opportunity to work on large-scale data, knowledge graph, and AI-driven initiatives</li><li>Staff-level candidates may provide architectural leadership, mentorship, and engineering guidance</li><li>Candidates must be authorized to work in the United States and satisfy applicable regulatory employment requirements</li></ul>
<p>Robert Half Management Resources is looking for a Business Analytics/Data Analyst to support one of our clients on an interim basis. This role will contribute to Workday supply chain and procurement efforts by improving data quality, validating converted records, and resolving issues that affect system readiness. The position works closely with project stakeholders to ensure legacy information is accurately prepared, tested, and aligned with business rules in the target environment.</p><p><br></p><p><u>Responsibilities:</u></p><p>• Prepare, map, and transform legacy supply chain and procurement data for upload into Workday using approved conversion tools and templates.</p><p>• Perform detailed comparisons between source records and Workday data to confirm completeness, accuracy, and adherence to defined business rules.</p><p>• Investigate conversion failures and data-related defects, identify root causes, and coordinate fixes for issues affecting inventory, suppliers, requisitions, and purchasing records.</p><p>• Partner with functional leads, analysts, and implementation team members to address data discrepancies and keep conversion activities moving on schedule.</p><p>• Support unit, integration, and broader testing cycles by building reliable data sets and confirming converted information performs correctly in test environments.</p><p>• Use Excel, SQL, and related analysis tools to cleanse, reconcile, and organize large datasets for migration and validation activities.</p><p>• Track issues in project management or defect logging tools and provide clear updates on status, findings, and resolution progress.</p>
<p><strong>Machine Learning Engineer</strong></p><p><br></p><p><strong>Company Overview</strong></p><p>Based in Los Angeles, California, the company specializes in transforming complex, multi-source data into actionable insights through machine learning, knowledge graph technologies, and advanced analytics. This is an opportunity to work on mission-critical applications in a highly collaborative environment focused on innovation, scalability, and operational excellence.</p><p><br></p><p><strong>Role Summary</strong></p><p>The Machine Learning Engineer will play a critical role in designing, training, deploying, and optimizing machine learning models that operate on large-scale temporal, geospatial, relational, and unstructured datasets. This position requires an experienced engineer who can independently own the full machine learning lifecycle, from dataset development and model architecture selection to deployment, monitoring, and continuous improvement. The ideal candidate brings broad expertise across computer vision, natural language processing (NLP), geospatial analytics, MLOps, and large-scale production machine learning environments.</p><p><br></p><p><strong>Key Responsibilities</strong></p><ul><li>Design, train, evaluate, deploy, and optimize machine learning models across multiple production use cases.</li><li>Build predictive solutions for anomaly detection, forecasting, entity resolution, relationship prediction, risk assessment, and operational decision support.</li><li>Partner with data engineering teams to develop high-quality training datasets from structured, unstructured, temporal, relational, and geospatial data sources.</li><li>Design model architectures and select appropriate algorithms based on business objectives, data characteristics, and operational requirements.</li><li>Develop and maintain machine learning pipelines spanning data preparation, feature engineering, training, evaluation, deployment, and monitoring.</li><li>Build scalable solutions that leverage graph-based and knowledge graph-driven data architectures.</li><li>Develop models utilizing computer vision, NLP, geospatial analytics, and predictive modeling techniques.</li><li>Establish rigorous evaluation frameworks, baselines, performance metrics, and validation methodologies.</li><li>Design experiments that mitigate data leakage, model drift, bias, and changing data distributions.</li><li>Implement monitoring, observability, alerting, retraining, rollback, and model governance processes.</li><li>Maintain reproducible datasets, model artifacts, evaluation results, and deployment workflows.</li><li>Collaborate with distributed engineering teams to deliver reliable and scalable machine learning capabilities.</li><li>Improve model calibration, confidence scoring, uncertainty estimation, and explainability.</li><li>Contribute to technical architecture, machine learning standards, and long-term platform strategy.</li></ul><p><strong>Additional Details</strong></p><ul><li>Fully onsite 5 days a week</li><li>Highly collaborative environment with strong emphasis on machine learning, knowledge graphs, and data-driven decision support</li><li>Opportunity to influence technical direction, machine learning standards, and model lifecycle practices across multiple initiatives</li><li>Candidates must be authorized to work in the United States and satisfy applicable regulatory employment requirements</li></ul>
<p>obert Half is seeking a Software Engineer II – AI Engineer to analyze, design, program, debug, test, implement, and support the privacy, security, and governance controls that protect generative AI technologies. This role safeguards GenAI-enabled applications, including LLM-powered workflows, RAG pipelines, plugins, skills, and autonomous agents, by embedding data protection, security guardrails, and responsible AI governance across the development lifecycle. </p><p> </p><p>This role supports SDLC documentation across all phases, with a focus on data privacy, security guardrails, governance, compliance, and risk management. It also works with users to define requirements and support applications in production. </p><p><strong>What You’ll Do</strong> </p><ul><li>Design and implement data privacy controls, including PII/PHI detection, redaction, and data minimization. </li><li>Build security guardrails, including prompt-injection defense, jailbreak prevention, and output filtering. </li><li>Establish governance for plugins, skills, and agents, including registration, approval, and lifecycle management. </li><li>Review and vet third-party plugins, skills, and agent tools for security, privacy, and compliance risks. </li><li>Define and enforce access controls, authentication, and least-privilege permissions for AI components. </li><li>Implement guardrails for autonomous agents, including action scoping, tool-use restrictions, and human-in-the-loop approval. </li><li>Apply data classification, retention, and residency policies across GenAI data flows. </li><li>Monitor AI systems for policy violations, data leakage, and anomalous plugin, skill, or agent behavior. </li><li>Maintain audit trails and logging for plugin, skill, and agent activity. </li><li>Support compliance with regulations and frameworks, including GDPR, CCPA, and the EU AI Act. </li><li>Provide Level II production support for security, privacy, and governance incidents. </li><li>Support incident response, including containment, escalation, and remediation. </li></ul>
<p>We are looking for a Content Specialist to support large-scale content conversion and migration initiatives. This is a high-volume, execution-focused role that requires exceptional organization, accuracy, and consistency. This Long-term Contract position will work across member-facing help content and internal knowledge resources, applying established content standards, style guidelines, and formatting requirements while helping improve content accessibility and usability. </p><p><br></p><p>Responsibilities:</p><ul><li>Convert process workflows from a decision-tree format into clear, structured wiki documentation while maintaining logical accuracy.</li><li>Review and quality-check content migrated into a new CMS, validating formatting, metadata, links, and overall content integrity.</li><li>Perform manual content migration when needed, ensuring accuracy and consistency.</li><li>Organize content within libraries and folders to improve discoverability, governance, and reuse.</li><li>Develop reusable content components, templates, and modules to streamline future content updates.</li><li>Apply established style, voice, and formatting standards consistently across all content.</li><li>Utilize AI and generative tools to support content conversion and QA activities, while maintaining ownership of final quality.</li><li>Collaborate with content strategists and cross-functional stakeholders to resolve questions regarding content accuracy, structure, and scope.</li><li>Exercise sound judgment when handling complex or non-standard content and provide recommendations for future content management approaches.</li><li>Track project milestones, monitor progress, and proactively identify risks or blockers.</li></ul>