We are looking for an experienced Lead Data Engineer to oversee the design, implementation, and management of advanced data infrastructure in Houston, Texas. This role requires expertise in architecting scalable solutions, optimizing data pipelines, and ensuring data quality to support analytics, machine learning, and real-time processing. The ideal candidate will have a deep understanding of Lakehouse architecture and Medallion design principles to deliver robust and governed data solutions.<br><br>Responsibilities:<br>• Develop and implement scalable data pipelines to ingest, process, and store large datasets using tools such as Apache Spark, Hadoop, and Kafka.<br>• Utilize cloud platforms like AWS or Azure to manage data storage and processing, leveraging services such as S3, Lambda, and Azure Data Lake.<br>• Design and operationalize data architecture following Medallion patterns to ensure data usability and quality across Bronze, Silver, and Gold layers.<br>• Build and optimize data models and storage solutions, including Databricks Lakehouses, to support analytical and operational needs.<br>• Automate data workflows using tools like Apache Airflow and Fivetran to streamline integration and improve efficiency.<br>• Lead initiatives to establish best practices in data management, facilitating knowledge sharing and collaboration across technical and business teams.<br>• Collaborate with data scientists to provide infrastructure and tools for complex analytical models, using programming languages like Python or R.<br>• Implement and enforce data governance policies, including encryption, masking, and access controls, within cloud environments.<br>• Monitor and troubleshoot data pipelines for performance issues, applying tuning techniques to enhance throughput and reliability.<br>• Stay updated with emerging technologies in data engineering and advocate for improvements to the organization's data systems.
<p>We are seeking a talented and motivated Python Data Engineer to join our global team. In this role, you will be instrumental in expanding and optimizing our data assets to enhance analytical capabilities across the organization. You will collaborate closely with traders, analysts, researchers, and data scientists to gather requirements and deliver scalable data solutions that support critical business functions.</p><p><br></p><p>Responsibilities</p><ul><li>Develop modular and reusable Python components to connect external data sources with internal systems and databases.</li><li>Work directly with business stakeholders to translate analytical requirements into technical implementations.</li><li>Ensure the integrity and maintainability of the central Python codebase by adhering to existing design standards and best practices.</li><li>Maintain and improve the in-house Python ETL toolkit, contributing to the standardization and consolidation of data engineering workflows.</li><li>Partner with global team members to ensure efficient coordination and delivery.</li><li>Actively participate in internal Python development community and support ongoing business development initiatives with technical expertise.</li></ul>
<p>Architect and deliver modern data platform solutions with a strong emphasis on Databricks and contemporary cloud data technologies.</p><p>Build secure, scalable, and high‑performing data environments that enable analytics, reporting, and enterprise‑wide data initiatives.</p><p>Oversee and execute migrations from legacy relational databases into Databricks-based ecosystems.</p><p>Design and structure scalable data pipelines and foundational data infrastructure aligned with organizational goals.</p><p>Create and maintain ETL/ELT processes within Databricks to ensure efficient ingestion, transformation, and delivery of data.</p><p>Continuously refine and optimize data workflows to improve performance, stability, and data quality across all processes.</p><p>Manage end-to-end data transitions to ensure operational continuity with minimal business disruption.</p><p>Monitor Databricks workloads and optimize performance, scalability, and cost efficiency across compute and storage layers.</p><p>Partner with data engineers, scientists, analysts, and product stakeholders to gather requirements and build fit‑for‑purpose data solutions.</p><p>Establish and enforce data engineering best practices, development standards, and architectural guidelines.</p><p>Assess emerging tools and technologies to enhance pipeline efficiency, reliability, and automation capabilities.</p><p>Provide technical direction, guidance, and mentorship to junior engineers and team members.</p><p>Collaborate closely with DevOps and infrastructure teams to deploy, manage, and support data systems in production.</p><p>Ensure all data solutions meet compliance standards, organizational security policies, and regulatory obligations.</p><p>Work with enterprise architects and IT leadership to align data architecture with broader technology strategies and long-term roadmaps</p>
<p>Position Overview</p><p>We are seeking a Data Governance & Data Quality Platform Engineer to own the technical administration, integration, and optimization of enterprise data governance and data quality platforms (e.g., Atlan, Monte Carlo). This role ensures governance and quality tools are scalable, securely integrated into the enterprise data ecosystem, and maintained for high availability and performance.</p><p>The ideal candidate brings strong platform engineering skills, experience automating data quality and metadata workflows, and a solid understanding of governance, compliance, and modern data architectures.</p><p>Key Responsibilities</p><p><br></p><p>1. Platform Engineering & Administration</p><ul><li>Configure and maintain data governance platforms for metadata management, data lineage, and governance workflows</li><li>Configure data quality tools for profiling, rule creation, and monitoring dashboards</li><li>Manage platform security, including user roles, authentication, SSO, RBAC, and access controls</li></ul><p>e2. Integration & Automation</p><ul><li>Develop and maintain integrations across data sources, databases, data lakes, and BI tools</li><li>Automate metadata ingestion and data quality checks using APIs, Python scripts, or ETL frameworks</li><li>Configure and maintain connectors for analytics and reporting platforms</li></ul><p> 3. Performance, Reliability & Monitoring</p><ul><li>Monitor platform health and optimize performance and scalability</li><li>Apply upgrades, patches, and troubleshoot technical issues</li><li>Implement logging, alerting, and proactive monitoring for governance and data quality environments</li></ul><p>a4. Technical Support & Issue Resolution</p><ul><li>Provide Tier 3 support for platform‑related incidents and escalations</li><li>Debug integration failures and resolve configuration conflicts</li><li>Collaborate with vendors for advanced troubleshooting and roadmap alignment</li></ul><p>r5. Security, Compliance & Risk Management</p><ul><li>Ensure platforms comply with data privacy and security standards (e.g., GDPR, CCPA)</li><li>Implement encryption, audit logging, and access controls</li><li>Support compliance reporting and risk assessments using governance and data quality metrics</li></ul>
<p>As our portfolio of AI-driven solutions continues to expand, we’re looking for an experienced <strong>Machine Learning Engineer</strong> to join our high-impact data science team. This role offers the opportunity to work across trading, operations, and support functions—delivering production-grade machine learning systems that solve real business problems.</p><p>You’ll collaborate with data scientists, software engineers, and commercial stakeholders to design, build, and deploy models that drive decision-making and innovation. From project scoping to model deployment, you’ll have visibility and influence across the full ML lifecycle.</p><p>🔧 Core Responsibilities</p><ul><li>Act as a thought partner to commercial teams, identifying high-value opportunities for AI/ML applications</li><li>Lead the design, development, and deployment of machine learning systems, with a focus on <strong>NLP</strong>, <strong>LLMs</strong>, and <strong>Generative AI</strong></li><li>Prioritize projects based on business impact and evolving market conditions</li><li>Collaborate with cross-functional teams to gather requirements and align solutions with strategic goals</li><li>Integrate ML solutions—including GenAI—into existing platforms to ensure seamless user experiences and scalable adoption</li><li>Participate in code reviews, experiment design, and tooling decisions to maintain high engineering standards</li><li>Share knowledge and mentor colleagues to build machine learning fluency across the organization</li></ul><p><br></p>
<p><br></p><p>Software Platform Engineer will design, build, and maintain a core Data & Machine Learning platform.</p><p><br></p><p>Platform Development: Design and implement new features for our AWS and Databricks-based platform, staying current with industry trends and advancements in AI. Core Component Implementation: Test and integrate central platform components that support our technology stack and serve tenants across the organization. Collaboration: Partner with other engineering teams to identify and deliver platform enhancements that solve specific business problems. Maintain Excellence: Uphold strict security protocols, compliance controls, and architectural principles in all aspects of your work.</p><p><br></p><p><br></p>
We are looking for an experienced Business Analyst to join our team in Houston, Texas. This role involves analyzing business processes, identifying improvement opportunities, and collaborating with cross-functional teams to deliver effective solutions. The ideal candidate will bring a strong analytical mindset, excellent communication skills, and a proven ability to thrive in dynamic environments.<br><br>Responsibilities:<br>• Conduct in-depth analysis of business processes to identify areas for improvement and optimization.<br>• Collaborate with stakeholders to gather requirements and translate them into actionable plans.<br>• Facilitate discussions with teams to ensure alignment with project goals and objectives.<br>• Review and analyze documentation to ensure accuracy and completeness.<br>• Provide support for call center operations by evaluating customer service processes and recommending enhancements.<br>• Apply Agile and Scrum methodologies to manage projects and ensure timely deliverables.<br>• Perform gap analysis to identify discrepancies between current and desired states, proposing solutions to bridge those gaps.<br>• Prepare detailed reports and presentations to communicate findings and recommendations.<br>• Work closely with technical teams to implement solutions that address identified needs and challenges.<br>• Maintain a proactive approach to problem-solving, ensuring business goals are met efficiently.
<p><strong>Principal Data Scientist (AI/ML Focus)</strong></p><p><strong>Service Type:</strong> 42 Week Contract </p><p><strong>Worksite:</strong> Onsite, Monday–Thursday — Houston, TX</p><p><strong>Pay: </strong>Available on W2 </p><p><strong>Position Overview</strong></p><p>We are seeking a <strong>Principal Scientist, Data</strong> with deep expertise in <strong>AI, Machine Learning, Natural Language Processing (NLP), Computer Vision (CV), and Generative AI</strong>. This role requires a strong technical foundation, excellent communication skills, and the ability to translate complex methodologies into meaningful business outcomes.</p><p>The ideal candidate is proactive, innovative, and passionate about developing advanced AI-driven solutions using modern architectures including <strong>LLMs, deep learning models, multi-agent systems, and generative AI techniques</strong>.</p><p><strong>Requirements</strong></p><ul><li>Strong background in <strong>NLP, Computer Vision, and Generative AI</strong>.</li><li>Broad background in <strong>Artificial Intelligence</strong>.</li><li>Excellent verbal and written communication skills.</li></ul><p> <strong>Key Responsibilities</strong></p><ul><li>Develop, train, and optimize <strong>machine learning and deep learning models</strong>.</li><li>Build advanced AI solutions using <strong>LLMs, multi-agent systems, fine-tuning techniques, and inference optimization</strong>.</li><li>Transform complex data science methodologies into actionable insights.</li><li>Collaborate closely with stakeholders to develop high-value, data-driven solutions.</li><li>Create clear, compelling presentations, dashboards, and deliverables for non-technical audiences.</li><li>Drive full lifecycle AI/ML projects from ideation through deployment.</li></ul>