We are looking for a Senior Data Engineer to develop and optimize enterprise data systems that support analytics and digital solutions. In this role, you will design and implement robust data architectures, ensuring seamless data integration and transformation processes across the organization. Your expertise will drive the creation of reliable pipelines and scalable infrastructure, enabling advanced analytics and machine learning capabilities.<br><br>Responsibilities:<br>• Design and implement scalable data pipelines using Databricks, Spark, and Delta Lake to support enterprise-level analytics.<br>• Develop and maintain efficient data models tailored for AI, analytics, and operational systems.<br>• Lead Master Data Management initiatives to establish unified and accurate data records across platforms.<br>• Create batch and near-real-time data processing workflows for structured and semi-structured datasets.<br>• Collaborate with AI and software development teams to ensure delivery of high-quality datasets for machine learning.<br>• Define and enforce data architecture standards, ensuring scalability, reliability, and governance.<br>• Troubleshoot and optimize data systems to maintain performance and reliability in complex environments.<br>• Partner with cloud and IT teams to integrate modern data platforms and ensure seamless functionality.
<p><strong>Overview</strong></p><p>We are seeking an experienced Data Engineer to join a growing data team supporting enterprise analytics, reporting, and data science initiatives. This role will focus on supporting and enhancing existing data pipelines while contributing to new development efforts within a modern data environment. The ideal candidate will have strong experience with Databricks, SQL, and Python, along with the ability to partner directly with business stakeholders and translate business requirements into scalable technical solutions.</p><p> </p><p><strong>POSITION: DATA ENGINEER</strong></p><p><strong>LOCATION: DALLAS, TX ONSITE</strong></p><p><strong>DURATION: 6-MONTH CONTRACT-TO-HIRE</strong></p><p> </p><p><strong>RESPONSIBILITIES</strong></p><ul><li>Develop, support, and enhance ETL and data pipeline solutions.</li><li>Design and build data transformations across Bronze, Silver, and Gold data layers within a Medallion Architecture environment.</li><li>Write, optimize, and troubleshoot complex SQL queries to support reporting, analytics, and operational business needs.</li><li>Develop and maintain data processing solutions using Python and PySpark within Databricks.</li><li>Partner directly with business stakeholders to gather requirements and translate business needs into technical solutions.</li><li>Monitor, troubleshoot, and resolve data pipeline issues while performing root cause analysis.</li><li>Design and maintain data warehouse and database models that support reporting, analytics, and data science initiatives.</li><li>Deliver trusted and certified datasets that power dashboards, self-service analytics, and business reporting.</li><li>Apply best practices related to data quality, testing, monitoring, performance optimization, documentation, and maintainability.</li><li>Collaborate with data engineers, analysts, data scientists, and business teams on strategic initiatives and ongoing support efforts.</li></ul>
We are looking for a Data Engineer to join our team in Texas. In this role, you will create and enhance modern cloud data platforms that power reporting, analytics, and intelligent business solutions. The position is ideal for a hands-on individual who excels at building scalable pipelines, improving data performance, and partnering with technical and business teams to deliver reliable data products.<br><br>Responsibilities:<br>• Create and maintain cloud-based data solutions on Azure using services such as Databricks, Synapse Analytics, Data Factory, and related platform tools.<br>• Build resilient data pipelines and integration workflows that support enterprise analytics, reporting, and business intelligence across large and complex datasets.<br>• Architect and implement lakehouse and warehouse environments, including layered data models that organize raw, refined, and business-ready data.<br>• Develop reusable notebooks and processing frameworks with Python, PySpark, Spark, and Scala to support scalable engineering patterns.<br>• Tune and troubleshoot data workloads to improve speed, stability, scalability, and overall cost efficiency in production environments.<br>• Deliver end-to-end data solutions by handling design, development, testing, deployment, documentation, and ongoing operational support.<br>• Apply source control, DevOps practices, and automated release processes to enable consistent and secure deployments.<br>• Partner with stakeholders, engineers, and leadership to convert business needs into practical technical designs and data solutions.<br>• Support AI and machine learning initiatives through data preparation, feature development, curated datasets, and platform capabilities for advanced analytics.<br>• Investigate production issues, evaluate new technologies, and recommend improvements that strengthen reliability, efficiency, and business value.
We are looking for a Data Engineer to join a growing team and contribute to the delivery of reliable, analytics-ready data solutions. This contract opportunity with potential for a permanent role is ideal for someone who enjoys building scalable data pipelines, improving data models, and partnering with technical and business stakeholders to support reporting, self-service analytics, and data science. The role offers the chance to work hands-on with Databricks, Python, PySpark, SQL, and modern data engineering practices in a collaborative environment focused on quality and performance.<br><br>Responsibilities:<br>• Build, maintain, and enhance data pipelines that support dependable data availability for analytics and reporting needs.<br>• Collaborate with data engineering leaders to troubleshoot defects, resolve pipeline issues, and improve overall platform stability.<br>• Develop transformation logic using Python, PySpark, SQL, and Databricks to prepare clean, usable datasets for downstream consumers.<br>• Design and refine data models that improve usability, consistency, and performance across reporting and analytical workloads.<br>• Apply layered data architecture principles, including Bronze, Silver, and Gold structures, to organize and manage data effectively.<br>• Establish and follow engineering standards for validation, testing, monitoring, and documentation to strengthen data quality and maintainability.<br>• Optimize processing and query performance to support efficient data delivery at scale.<br>• Work with business and technical partners to translate data needs into practical engineering solutions that support trusted insights.
<p><strong>Overview</strong></p><p>We are seeking an experienced Data Analyst to support a large-scale healthcare data and reporting transformation initiative. This role is ideal for a hands-on analyst who is comfortable working with complex datasets, building reports and dashboards, and partnering with business stakeholders to ensure critical reporting remains accurate and available during system transitions. You will play a key role in maintaining reporting continuity, supporting data validation efforts, and delivering actionable insights in a fast-paced environment.</p><p><br></p><p><strong>POSITION: DATA ANALYST</strong></p><p><strong>LOCATION: REMOTE</strong></p><p><strong>DURATION: CONTRACT</strong></p><p><br></p><p><strong>RESPONSIBILITIES:</strong></p><ul><li>Develop, maintain, and enhance reports and dashboards to support business and operational reporting needs.</li><li>Migrate and rebuild reporting assets as data sources and reporting requirements evolve.</li><li>Create and optimize SQL queries to extract, analyze, and validate data from enterprise data warehouse environments.</li><li>Build, maintain, and enhance Power BI reports, dashboards, and semantic data models.</li><li>Analyze reporting requirements and translate business needs into actionable data solutions.</li><li>Validate data accuracy, troubleshoot reporting discrepancies, and partner with technical teams to resolve issues.</li><li>Support data integration, reporting continuity, and analytics initiatives during system and process changes.</li><li>Collaborate with business stakeholders, analysts, and technical teams to prioritize and deliver reporting requests.</li><li>Document report logic, data sources, business rules, and reporting processes.</li><li>Provide analytical support across multiple projects while managing shifting priorities and deadlines.</li></ul>
We are looking for a Senior Data Engineer to help shape and expand a modern enterprise data ecosystem in Dallas, Texas. This role is ideal for a highly technical specialist who enjoys building scalable cloud-based data solutions, improving platform performance, and collaborating with both engineering and business leaders. The position offers the opportunity to contribute directly to lakehouse design, advanced pipeline development, and data initiatives that support analytics and emerging AI use cases.<br><br>Responsibilities:<br>• Architect and develop scalable data platforms using Azure Databricks, Spark, PySpark, Python, Delta Lake, and related big data technologies.<br>• Create and maintain layered lakehouse data models across raw, refined, and curated environments to support enterprise reporting and analytics.<br>• Lead the movement of legacy data assets into Azure-based cloud environments as part of broader platform modernization efforts.<br>• Build, enhance, and monitor high-volume ETL and streaming workflows, notebooks, and distributed processing jobs for reliability and efficiency.<br>• Integrate tools such as Azure Synapse Analytics and Azure Data Factory to support end-to-end data ingestion, transformation, and delivery.<br>• Improve performance of large-scale data workloads by tuning Spark jobs, optimizing code, and applying engineering best practices.<br>• Establish and follow modern software delivery standards through source control, Azure DevOps pipelines, CI/CD processes, and deployment automation.<br>• Prepare and structure data for machine learning, generative AI, and MLOps initiatives, including feature creation and production-ready integration.<br>• Work closely with implementation partners, architects, technical teams, business stakeholders, and senior leadership to align solutions with organizational goals.<br>• Contribute hands-on engineering expertise while also helping guide technical design and platform architecture decisions.
<p>Seeking a hands-on Data Platform Engineer to build and operate the AWS infrastructure supporting an enterprise analytics environment. This is an engineering-focused position centered on <strong>building pipelines, orchestration, and cloud infrastructure</strong>, not dashboards or business analysis.</p><p><br></p><p>What You'll Do</p><ul><li>Build and maintain an AWS-based data platform and Amazon Redshift environment.</li><li>Code and build data pipelines using Python, dbt, and AWS Lambda.</li><li>Work across AWS services including S3, Glue, Step Functions, IAM, and CloudWatch.</li><li>Develop ETL/ELT processes and data transformation workflows.</li><li>Monitor pipeline health and troubleshoot failures, latency, and infrastructure issues.</li><li>Build for reliability, scalability, security, and cloud cost efficiency.</li><li>Support data ingestion into the warehouse environment.</li><li>Maintain data-flow documentation and operational runbooks.</li><li>Translate analytics requirements into technical pipeline and infrastructure solutions.</li><li>Recommend automation and architectural improvements as the platform grows.</li></ul>
We are looking for a Software Engineer to join a services team in Plano, Texas, in a contract capacity with the potential for a permanent role. This role focuses on building and supporting cloud-native applications that rely on scalable, event-driven microservices and modern engineering practices. The ideal candidate brings strong experience with distributed systems, cloud platforms, and collaborative delivery in a fast-moving environment.<br><br>Responsibilities:<br>• Create and enhance cloud-native microservices that support high availability, strong performance, and long-term scalability.<br>• Develop event-driven application components and integrate messaging patterns through publish-and-subscribe architectures.<br>• Build, package, and manage services with container technologies while supporting orchestration in Kubernetes environments.<br>• Partner with DevOps and infrastructure teams to streamline deployments, strengthen CI/CD workflows, and improve production reliability.<br>• Investigate and resolve issues affecting system responsiveness, uptime, and application stability across distributed services.<br>• Work with cross-functional stakeholders to translate business needs into technical solutions and deliver well-designed software increments.<br>• Participate in peer code reviews, share engineering best practices, and provide guidance to less experienced developers.<br>• Contribute to production support efforts by diagnosing incidents and implementing sustainable fixes.<br>• Evaluate tools, frameworks, and architectural approaches, offering clear technical rationale for design decisions.<br>• Maintain awareness of emerging trends in cloud engineering, microservices, and software delivery practices.
<p><br></p><p>Our client is seeking a Generative AI Engineer to design, build, and deploy AI-powered applications that improve business processes and user experiences. This role will focus on leveraging Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and cloud-based AI services to deliver scalable, production-ready solutions.</p><p><br></p><p><strong>POSITION TITLE:</strong> Generative AI Engineer</p><p><strong>LOCATION:</strong> Dallas, TX (Hybrid)</p><p><strong>SALARY:</strong> $130,000 - $160,000</p><p><br></p><p><strong>RESPONSIBILITIES</strong></p><ul><li>Design, develop, and deploy generative AI applications utilizing Large Language Models and modern AI frameworks.</li><li>Build AI copilots, chatbots, virtual assistants, and knowledge management solutions using Azure OpenAI and related technologies.</li><li>Develop and optimize Retrieval-Augmented Generation (RAG) architectures utilizing vector databases and enterprise data sources.</li><li>Integrate AI solutions with existing business applications, APIs, and cloud platforms.</li><li>Monitor, troubleshoot, and enhance AI application performance, accuracy, security, and scalability.</li></ul><p><br></p>
We are looking for a Computer Vision - AI Engineer to join a team in Coppell, Texas, on a Long-term Contract assignment. In this role, you will design and refine vision-based AI solutions that support real-world image and video use cases, while partnering with cross-functional teams to move ideas from research into production. The position is ideal for someone who combines strong machine learning expertise with practical understanding of hardware constraints and model performance in live environments.<br><br>Responsibilities:<br>•Design, build, and enhance computer vision and machine learning models for use cases involving object detection, image segmentation, classification, and video-based analysis.<br>•Compare modeling approaches, assess trade-offs across accuracy, speed, and scalability, and recommend fit-for-purpose solutions aligned with business goals.<br>•Train and adapt deep learning models using frameworks such as PyTorch or TensorFlow, applying techniques like transfer learning and optimization for efficient performance.<br>•Establish evaluation methods, track key performance measures, investigate error patterns, and iterate on models to improve reliability and response time.<br>•Incorporate practical considerations related to cameras, sensors, lighting conditions, and edge hardware when developing and tuning solutions.<br>•Create and support data preparation, annotation, and validation workflows that enable consistent experimentation and dependable deployment.<br>•Work closely with software, hardware, and MLOps partners to transition models from proof of concept into production-ready applications.<br>•Contribute to deployment readiness by helping optimize models for inference and operational use in resource-constrained environments.