Cuesta Partners

Solutions Engineer

• Design cloud-native AI and data solution architectures, including reference patterns, data flows, AI/ML workflows, and LLM orchestration that balance scalability, security, cost, and speed to value.
• Translate AI and data architectures into clear diagrams, executive-ready narratives, visuals, and roadmaps for technical and non-technical stakeholders.
• Advise clients on AI strategy, build-vs-buy decisions, governance, and ethical considerations.
• Integrate and orchestrate data across multiple platforms, including cloud data warehouses and lakes (e.g., Snowflake, BigQuery, Databricks) and external APIs
• Collaborate with ML Engineers and Data Engineers to validate architectural alignment and integration feasibility.
• Conduct architecture reviews and risk assessments; identify and execute course corrections as needed.
• Architect AI solutions that leverage LLMs and RAG patterns to deliver interpretable insights and human-readable outputs from complex model results.
• Mentor junior architects and consultants; contribute to reusable accelerators, templates, and an internal knowledge base.
• Provide technical oversight across multiple client engagements, ensuring architectural consistency, delivery quality, and alignment between business requirements and implemented solutions.
• Provide hands-on guidance to engineering and data teams on API integrations, data pipelines, and AI service connectivity, ensuring solutions are scalable, secure, and maintainable.
• Stay current on emerging AI platforms, LLM tooling, and cloud-based data services, continuously evaluating how they can enhance our integration capabilities.

AI Solutions Architect

• Design cloud-native data and AI architectures (reference patterns, data models, MLOps pipelines, LLM orchestration) that balance scalability, security, cost, and speed to value.
• Translate complex technical concepts into executive-ready narratives, visuals, and roadmaps.
• Advise clients on AI strategy, build-vs-buy decisions, governance, and ethical considerations.
• Provide hands-on guidance to developers, data engineers, and ML engineers; review code, pipelines, and infrastructure for best-practice adherence.
• Conduct architecture reviews and risk assessments; identify and execute course corrections as needed.
• Mentor junior architects and consultants; contribute to reusable accelerators, templates, and an internal knowledge base.
• Evaluate emerging AI/ML tools, frameworks, and cloud services to keep our stack forward-leaning and competitive.

Solutions Architect

Analytics Engineer

• Develop data pipelines: develop code for data ingestion interfaces to meet specifications required by analytics teams, following best practices, company standards, and adhering to data models outlined by data architects – making design recommendations as appropriate.
• Work alongside our business consultants and product managers in engaging and partnering with client stakeholders in order to create effective and strategic solutions.
• Stay abreast of emerging technologies and industry trends, identifying new tools and practices that can help the company achieve its strategic goals.
• Establish close communication with team members to align best practices, exchange information effectively, and collaborate for continuous improvement.
• Quickly triage and diagnose system or performance issues and support the business during critical downtimes and releases.
• Act as the technical expert, proposing solutions to roadblocks, outlining dependencies/constraints, and escalating problems as appropriate.
• Establish consistent communication mechanisms with project delivery teams.
• Establish relationships across the organization to build networks and leverage experts.

Consultant

• Executing workplan components and developing quality client deliverables
• Managing project plans to ensure timely delivery of project elements
• Translating project efforts into client value they produce
• Engaging technical and non-technical team members to support communication to client
• Mapping business objectives into technological capabilities
• Processing extensive, diverse information into concise, professional client deliverables
• Interpreting information and translating insights into optionality and/or pathways
• Leveraging company and industry frameworks to glean strategic insights and formulate recommendations
• Using logic and reasoning to solve ambiguous problems
• Distilling complex business operations
• Conducting research and analysis to assimilate information and develop perspectives
• Managing stakeholder relationships and expectations across client teams, often at the executive level

Associate Consultant

Data Analyst

• Establish close communication with team members to align best practices, exchange information effectively, and collaborate for continuous improvement.
• Quickly triage and diagnose system or performance issues and support the business during critical downtimes and releases.
• Act as the technical expert, proposing solutions to roadblocks, outlining dependencies/constraints, and escalating problems as appropriate.
• Collecting and organizing information from various sources.
• Cleaning and transforming data to ensure accuracy and consistency.
• Applying statistical analysis methods to identify trends and patterns.
• Developing models and visualizations to present insights clearly and concisely.
• Conducting exploratory data analysis to uncover relationships and correlations.
• Collaborating with stakeholders to understand business requirements and provide data-driven recommendations.
• Monitoring and evaluating quality to ensure reliability and integrity.
• Creating reports and dashboards to familiarize customers with the insight and results of the analysis.
• Establish consistent communication mechanisms with project delivery teams.
• Support the business during critical downtimes and releases.
• Follow all IT auditing and systems access control processes.
• Support interfaces as necessary through troubleshooting, bug-fix, and escalations.
• Mentor junior developers to improve team performance and empower others.
• Establish relationships across the organization to build networks and leverage experts.

Data Engineer

• Design the business data model based on the discovered business processes and data analysis
• Translate business requirements into technical design specifications, including data streams, integrations, transformations, databases, and data warehouses.
• Develop work estimates for Data Warehouse & Data Lake deliverables
• Coach and mentor a team of a few dozen data engineers, analysts and ML Engineers on data architecture and modeling best practices
• Define the data architecture framework, standards, and principles, including modeling, metadata, security, reference data such as product codes and client categories, and master data such as clients, vendors, materials, and employees
• Define reference architecture, which is a pattern others can follow to create and improve data systems
• Define data flows, i.e., which parts of the organization generate data, which require data to function, how data flows are managed, and how data changes in transition
• Collaborate and coordinate with team members, clients and external SMEs

Data Architect

Data Scientist

• Stay updated with the latest trends in data science and AI.
• Collect, clean, and preprocess large datasets to extract valuable insights.
• Develop predictive models and machine learning algorithms for business use
• cases.
• Collaborate with stakeholders to translate business problems into data-driven
• solutions.
• Use statistical analysis to optimize business processes.
• Communicate findings through data visualization and storytelling.

• Develop data pipelines: develop code for data ingestion interfaces to meet specifications required by analytics teams, following best practices, company standards, and adhering to data models outlined by data architects – making design recommendations as appropriate
• Stay abreast of emerging technologies and industry trends, identifying new tools and practices that can help the company achieve its strategic goals
• Establish close communication with team members to align best practices, exchange information effectively, and collaborate for continuous improvement
• Quickly triage and diagnose system or performance issues and support the business during critical downtimes and releases
• Act as the technical expert, proposing solutions to roadblocks, outlining dependencies/constraints, and escalating problems as appropriate
• Establish consistent communication mechanisms with project delivery teams
• Support the business during critical downtimes and releases
• Follow all IT auditing and systems access control processes
• Support interfaces as necessary through troubleshooting, bug-fix, and escalations
• Mentor junior developers to improve team performance and empower others
• Establish relationships across the organization to build networks and leverage experts

Engineer

• Build and optimize data models, pipelines, and visualizations to ensure scalability and impact.
• Develop and implement best practices for technology and analytics solutions.
• Collaborate with leadership to establish strategic insights for customers.
• Ensure data quality, accuracy, and integrity through rigorous standards and controls.
• Drive end-to-end data solutions, unlocking insights that enable data-driven decision-making.
• Utilize Power BI to design, develop, and maintain interactive and intuitive dashboards that showcase data trends, patterns, and anomalies.
• Integrate data from various sources, ensuring accuracy, consistency, and relevance.
• Implement best practices for data visualization, including effective use of charts, graphs, and other visual elements to enhance data storytelling.
• Contribute to the development and improvement of data visualization templates, standards, and guidelines.
• Explore innovative techniques and tools to enhance the quality and efficiency of data visualization processes.
• Collaborate with the broader team to identify opportunities for automation and optimization of data visualization workflows.
• Collaborate closely with engagement teams and clients to understand data requirements, objectives, and key performance indicators (KPIs).
• Translate complex data into meaningful and visually compelling dashboards that provide valuable insights and support data-driven decision-making.
• Effectively communicate complex technical concepts to non-technical stakeholders, enabling clear understanding and appreciation of data insights.

Data Visualization Engineer

Manager

• Leading workstreams as a part of a larger project; planning work according to project scope and timelines
• Managing stakeholder relationships and expectations across client teams, including at the executive level
• Owning deliverable story line development and ensuring the flow and level of detail is appropriately tailored to the audience
• Prioritizing work across multiple teams and facilitating trade-off discussions
• Identifying critical strategic gaps and designing solutions
• Processing extensive, diverse information and distilling that information into clear, concise, professional client deliverables
• Interpreting information and translating insights into optionality and / or pathways
• Assigning and delegating ownership of work to project team members
• Taking a proactive approach to identifying and managing issues
• Communicating updates, progress, and upcoming plans for the project to internal and client stakeholders
• Managing and mentoring junior team members
• Identifying, escalating, and addressing gaps in client and Cuesta team skills, capacity, or capabilities
• Using professional concepts and project objectives to resolve complex issues
• Generating insights and devising plans to address issues identified
• Supporting sales and account expansion efforts

• Lead end-to-end technology due diligence engagements across private equity transactions.
• Evaluate IT systems, software architecture, cloud infrastructure, data environments, cybersecurity, packaged software, and technical talent.
• Identify and assess scalability issues, technical debt, architectural risks, and operational deficiencies.
• Translate complex technical findings into clear business implications for investors and executives.
• Advise deal teams on risk mitigation, value creation opportunities, and post-transaction integration strategies.
• Mentor and guide consultants to ensure high-quality delivery across multiple concurrent engagements.
• Foster collaboration and elevate technical rigor within the team.
• Partner with leadership to develop new service offerings and improve delivery methodologies.
• Contribute to client relationship management and thought leadership initiatives.
• Support the firm’s growth through process innovation, cross-functional collaboration, and expanding practice capabilities.

Technical Manager – Private Equity

Full-stack Engineer

• Act as a subject matter expert in data engineering, providing guidance and hands-on support to resolve technical challenges and optimize performance.
• Lead architectural discussions, providing insights into design decisions and trade-offs.
• Lead the planning, execution, and delivery of client-facing and internal data projects, ensuring adherence to project timelines, scope, and quality standards.
• Collaborate with cross-functional teams, including Data Scientists, Analysts, and Business Stakeholders, to ensure seamless project execution.
• Collaborate closely with the Team Lead to define the technology strategy and roadmap for the Data Team.
• Serve as a data architect for client engagements, working closely with clients to understand their data requirements, design appropriate data architectures, and develop scalable solutions.
• Foster a collaborative and inclusive team culture, promoting knowledge sharing and continuous learning among team members.
• Identify opportunities to improve processes, tools, and methodologies, driving efficiency and innovation within the Data Team.