Team Leader
Featured Full Team of Data Analytics Specialists
Data Scientist and Developer
Previously at
Engineering Experience
13+ Years
Data Scientist and Developer
Previously at
Engineering Experience
12+ Years
Data Engineering Developer
Previously at
Engineering Experience
12+ Years
TRUSTED BY LEADING BRANDS
OUR SERVICES
Toptal’s Data Analytics services provide clients access to a global network of experts with specialty in optimizing data-driven enterprises and enhancing analytics capabilities. Utilize our expertise in analytics and data management to transform data into actionable insights.
CEO, Technology Services
As Toptal’s CEO of Technology Services, Robert leads strategy and operations across our technical services portfolio, spanning AI, automation, and operations. He previously served as Deloitte’s Managing Director & Chief Commercial Officer, transforming its Cloud Operate and Engineering business into a multibillion-dollar operation. He held senior roles at IBM, Velocity, co-founded Corio, and was CIO for two Fortune 100 manufacturers.
Previously at
Technology Experience
35+ Years
Delivery Manager
Rachael serves as a Delivery Manager at Toptal with a focus on leading diverse global teams in developing innovative solutions for our clients. She works across multiple disciplines, including technology, marketing, and management consulting. Rachael specializes in managing people and client relationships, process optimization, and driving teams toward optimal business outcomes.
Previously Managed Client
Experience
9+ Years
47+ Years
of Experience
Art has decades of data architecture and cloud-computing consulting experience—mostly in building enterprise data warehouses. Art is an end-to-end solution architect and chief problem solver with a long history of focused execution—according to a statement of work—and successful delivery in a team setting.
Previously at
18+ Years
of Experience
Emilio is a computer science engineer and MBA graduate with 18+ years of experience helping businesses define and analyze their KPIs across marketing, sales, operations, and finance. He is skilled in SQL and has been working with Tableau since 2011, developing all data integration required to feed Tableau from any data source. As a Tableau consultant and trainer, Emilio can quickly interpret the business needs and build insightful dashboards.
Previously at
8+ Years
of Experience
Joslyn is a seasoned data practitioner with demonstrated experience across multiple industries, including technology consulting and customer service. With her academic background in applied statistics and a skill set in machine learning, data analytics, Python, and SQL, Joslyn has delivered numerous projects with positive business impacts on customers.
Previously at
5+ years
of Experience
Guillermo is a senior product data analyst who has worked with CPOs, POs, and tech leads to develop profitable and appealing digital products. Combining his technical and business skills, he unlocks a lever to make data-driven decisions. He was involved in the design and analysis of 40+ OCEs for mobile and web digital products. Having worked in ecosystems of 20+ million WAU, Guillermo knows how to put apart vanity metrics to retrieve meaningful insights and propose actions to boost OEC.
Previously at
30+ Years
of Experience
Gabriel more than 30 years of experience installing, managing, and using SQL, NoSQL (key-value, columnar, graph), time-series, spatial, in-memory, and cloud databases. He's worked with 10TB OLTP databases, 10TB data warehouses, tables with 10+ billion rows, and databases with 10,000+ tables.
Previously at
17+ Years
of Experience
Thomas has 17+ years of experience in risk modeling and causal inference and has managed over €1 million in research funds as a scientist. Since 2019, he has worked as an independent contractor for various global pharmaceutical companies and CROs. His goal is to improve data-driven decision making by adopting state-of-the-art analysis methods and delivering scientific scrutiny in a timely fashion.
Previously at
24+ Years
of Experience
Denis is a senior full-stack AI engineer and data scientist, highly skilled in modern generative tech (GPT-4, Midjourney, and more), machine learning, ETL pipelines, data analysis, mathematical modeling, big data, and MLOps. He has a PhD in mathematics, and his data science expertise includes probabilistic risk modeling, revenue forecasting, geospatial data analysis, handwriting recognition, anomaly detection in time series, data engineering, and team leading.
Previously at
8+ Years
of Experience
Laura is a data-driven strategist with expertise in turning data into actionable insights. Laura fell in love with Tableau 7.0 in her MBA business analytics program and never looked back. Now, she is a Tableau certified professional and spends her days telling stories with data to influence business decisions.
Previously at
24+ Years
of Experience
Stephane possesses an impressive 24 years of international experience in data analytics and business process optimization. Throughout his career, he has excelled in various areas, including database design, business intelligence (BI), machine learning algorithms, big data, and predictive analytics. Stephane's expertise lies in developing solutions that automate processes, allowing clients to allocate their efforts toward value-added activities.
Previously at
7+ Years
of Experience
Daniel is a data engineer and architect specializing in Azure's cloud services. He's keen on collaborating with data analysts and data scientists to provide them with accurate data for dashboards and experiments, leveraging his clear communication skills and problem-solving mindset. As a certified Azure data engineer and a Databricks data engineer professional, Daniel's tech stack includes Databricks, Azure Data Factory, Azure Synapse, Azure Analysis Services, and Microsoft Power BI.
Previously at
13+ Years
of Experience
Hanee' is a data expert who enjoys working on data analytics and segmentation to better target customers with campaigns. He is an experienced Java developer who has built enterprise applications that interact with millions of customers daily. Hanee' also has experience working with big data, Spark, and Python.
Previously at
6+ Years
of Experience
Bilal is an experienced data scientist with solid knowledge of machine learning, data analysis, and visualization. He offers more than six years of professional experience in data science, working on projects related to building and deploying machine learning models, extracting insights from data, and managing data. Bilal is passionate about working with teams whose vision aligns with his values and specializations.
Previously at
13+ Years
of Experience
Ernesto is a business intelligence professional with more than 13 years in the field. With his expertise in data warehousing, Amazon Redshift, MS SQL Server, ETL (SSIS), Tableau Desktop, and Tableau Server, he helps companies integrate and organize data from multiple sources to be used for reporting and analysis. Ernesto’s attention to detail, strategic thinking, and expertise make him a highly qualified individual for various projects.
Previously at
14+ Years
of Experience
Mabrouk is a data warehouse architect with more than 14 years of experience, and he enjoys his job like the first day. He has hands-on experience modeling and developing data warehouses on top of leading systems like SQL Server, Google Cloud BigQuery, Snowflake, and Databricks.
Previously at
4+ Years
of Experience
Jorge is a Microsoft Certified: Data Analyst Associate and Azure Data Engineer Associate with Power Platform and Power BI certifications. He specializes in data analytics, business intelligence, data science, and SQL. Jorge has solid experience as a Power BI administrator and developer working for various enterprise clients in banking, finance, oil & gas, and business services.
Previously at
Elevate brand experiences with events and experiential marketing services
Achieve Data-Driven Success With Toptal’s Data Analytics Services
Discover the cutting-edge benefits our clients enjoy from the global Toptal network.
Challenge: Precision Drilling wanted to develop cutting-edge data visualization software that could accelerate the analysis of drilling rig performance data and provide increased value to its customers.
Solution: Toptal assembled a team to help Precision Drilling realize its vision. This team developed Clarity, a state-of-the-art data visualization platform that delivers real-time insights.
Outcome: The platform serves as a central hub for real-time and historical rig fleet performance data, significantly boosting efficiency and enabling instant communication of results across the organization.
Shuja Goraya, CTO, Precision Drilling
Newsweek and Statista’s rankings were based on an independent survey of more than 2,400 decision-makers at Fortune 500s.
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Highest ranked across all industries
Other Professional Services
Methodology for the Rankings
How likely the respondent is to recommend the selected company to others.
Measures the convenience of interaction with the company and efficiency of processes.
Measures the company’s cost-effectiveness and quality relative to price.
Measures whether the company consistently meets or exceeds expectations in quality and timeliness of deliverables.
Measures the company’s ability to consistently fulfill commitments and maintain customer trust.
COLLABORATION THAT WORKS
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Robert Orshaw
CEO, Technology Services
OUR THOUGHT LEADERSHIP
Read the latest articles and resources to stay informed about emerging trends in business intelligence, data, analytics, artificial intelligence and more.
Data inundates us like never before—how can we hope to analyze it? Graphs (networks, not bar graphs) provide an elegant approach. Find out how to start with the Python NetworkX library to describe, visualize, and analyze “graph theory” datasets.
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RELATED EXPERTISE
Pair Toptal’s data analytics specialists with related competencies to tackle your business technology initiatives extensively.
Data analytics has become a foundational capability for organizations seeking to improve decision-making, optimize operations, and strengthen long-term strategy execution. However, translating raw data into measurable business outcomes requires more than deploying dashboards or running reports. It takes careful planning, a solid foundation to work from, clear lines of authority, and making sure that your analytics efforts are actually aligned with what the business needs.
Data analytics providers give organizations the framework they need to move from fragmented reporting to a single, clear view of what’s going on. Rather than treating analysis as isolated tools or departmental experiments, businesses get the most out of analytics when it’s part of a bigger plan to transform how they use data. This type of service should act as a plan that ties data integration, governance, modeling, visualization, and decision-making all together.
What you get from a good data analytics service goes beyond producing insights. With solid guidance on how to structure things, organizations can start to focus on what really matters: high-impact initiatives, coordinating stakeholders across technical and business functions, and scaling analytics capabilities over time. This coordinated approach makes for better forecasting, more efficient operations, and a more mature analytics capability across the whole business.
This article explains how to plan, implement, and scale data analytics services engagements to deliver measurable and lasting business value.
Effective data analytics services engagements begin with alignment between business objectives and implementation strategy. Organizations typically achieve stronger results when analytics initiatives support measurable priorities such as revenue growth, operational efficiency, customer intelligence, or risk mitigation.
Planning begins by identifying high-value use cases where analytics services can deliver meaningful outcomes. These initiatives often include:
Establishing success metrics early ensures analytics initiatives remain outcome-focused rather than tool-driven. KPIs tied to business priorities create clarity across stakeholders and help measure the impact of implementation decisions.
Alignment with broader digital strategy also plays a central role. Analytics services deliver the strongest value when integrated with modernization initiatives such as cloud migration, enterprise data platform adoption, or governance transformation programs.
Organizations pursuing strategic initiatives such as M&A consulting engagements frequently rely on analytics capabilities to support due diligence, integration planning, and performance benchmarking across acquired assets. Structured analytics environments enable leadership teams to evaluate operational synergies and identify integration priorities more efficiently.
Clear stakeholder alignment among business leaders, technical teams, and analytics specialists establishes coordination at delivery checkpoints and strengthens adoption across the organization.
A structured planning phase ultimately enables the development of analytics capabilities that support both operational visibility and long-term strategic execution.
The type of data analytics services an organization needs depends on its maturity, infrastructure readiness, and strategic priorities. Some initiatives deliver quick improvements, while others focus on building analytics systems that support long-term change.
Balancing quick wins with long-term capability development helps organizations demonstrate early value while building scalable analytics foundations.
Analytics consulting support ensures implementation strategies align with broader business objectives rather than remaining isolated technical initiatives.
Organizations pursuing operational efficiency initiatives typically deploy analytics services to support:
Strategic analytics initiatives, by contrast, focus on:
Strategic analytics capabilities support executive planning and transformation programs such as digital modernization and M&A strategy alignment. They also help organizations use performance data to improve integration planning and capture expected value from acquisitions.
Startups frequently engage analytics services to support rapid experimentation, improve product analytics visibility, and track early-stage performance. Their implementation priorities typically emphasize flexibility, speed, and scalable architecture foundations.
Enterprise organizations typically require more structured analytics environments capable of integrating legacy systems, distributed data sources, and regulatory requirements.
Analytics services help manage complexity while aligning infrastructure planning with enterprise-wide data strategy objectives.
Differences in scale, governance maturity, and infrastructure readiness influence how analytics consulting engagements are structured across organizational environments.
Organizations typically engage data analytics services when internal teams need support with advanced analytics implementation, large-scale integration initiatives, and improvements in performance visibility.
Consulting support helps organizations evaluate feasibility early while reducing implementation risk across complex environments.
Early engagement improves the likelihood that analytics initiatives progress efficiently from planning to deployment while maintaining alignment with business priorities.
Selecting the right analytics consulting partner involves evaluating both technical capabilities and the ability to translate data into business outcomes.
Strong analytics partners typically contribute across multiple delivery phases, including:
Communication structure and delivery flexibility also influence long-term engagement success. Consulting teams that collaborate closely with internal stakeholders help ensure analytics outputs remain actionable rather than theoretical.
Organizations should also evaluate a partner’s ability to support transformation initiatives such as cloud modernization, enterprise reporting alignment, and integration support across programs, including M&A Consulting environments where data consolidation plays a critical role.
Past implementation results, transparency in delivery methodology, and scalability readiness all contribute to selecting an effective analytics partner.
Pricing for data analytics services typically reflects implementation complexity, infrastructure scale, and engagement structure.
Common delivery models | Cost considerations |
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Organizations that align high-quality analytics investments with measurable business outcomes are better positioned to evaluate return on investment across both short-term improvements and long-term analytics maturity development.
Structured analytics consulting engagements follow a predictable track that takes organizations from the early stages of data discovery through to delivering valuable insights and continually refining their approach.
Modern analytics programs bring together scalable platforms and robust delivery frameworks designed to keep things running smoothly across evolving operational environments.
Getting governance, Agile delivery methods, and quality control checkpoints to run smoothly is key to ensuring analytics outputs are accurate, secure, and compliant across the whole enterprise.
These project management and quality control measures can improve the reliability of your analytics outputs, while also making your efforts more sustainable in the long run.
High-performing analytics engagements typically follow a structured lifecycle that includes:
Successful analytics initiatives require strong alignment among how your infrastructure is set up, your governance framework, and how stakeholders work together.
Best practices help to keep things reliable as your analytics initiatives grow across different departments and business units.
Analytics works better when organizations bring fragmented data sources together into unified, accessible, high-performance systems.
Data integration and warehousing strategies support centralized reporting environments and scalable analytics pipelines, as well as improved cross-functional visibility and consistent governance enforcement.
Well-designed integration architectures enable organizations to build analytics ecosystems that support both operational reporting and enterprise-scale decision environments.
Reliable analytics outputs depend on structured governance frameworks that support accuracy and consistency across datasets.
Organizations strengthen data integrity by implementing:
Ongoing data quality management ensures analytics outputs remain reliable as operational environments evolve.
Visualization frameworks make complex datasets easier to understand by turning them into clear insights that stakeholders can use quickly.
Effective dashboards align reporting structures with business priorities while supporting performance monitoring, executive planning, and operational visibility.
Analytics storytelling improves adoption by connecting insights to business objectives instead of isolated technical outputs.
Self-service analytics lets non-technical stakeholders explore data independently and rely less on centralized teams.
These environments improve agility by accelerating reporting workflows, expanding access to insights, and supporting cross-functional collaboration.
Wider access to analytics strengthens organizational data literacy while improving responsiveness across decision-making environments.
Analytics consulting initiatives deliver better results when they’re woven into a company’s operations rather than treated as standalone reporting projects.
Collaborative delivery models bring business leaders, technical experts, and analytics teams together to work toward the same goals, fostering a culture of ongoing learning and improvement.
When teams collaborate, share ideas, and take ownership of projects, analytics programs see stronger adoption and deliver lasting value.
Organizations achieve stronger results when analytics initiatives remain closely aligned with measurable business priorities rather than isolated experimentation.
Structured prioritization frameworks help organizations allocate resources efficiently across high-impact initiatives.
Early analytics projects should prioritize use cases with clear financial or operational value, such as:
Delivering early wins strengthens organizational confidence while supporting continued analytics investment.
Sustained analytics maturity depends on structured roadmaps that align infrastructure, governance, and stakeholder adoption strategies.
Data strategy and governance frameworks support:
Organizations pursuing transformation initiatives such as digital modernization or M&A Consulting integration programs benefit particularly from structured governance alignment across datasets.
Strong analytics capabilities depend on collaboration between internal stakeholders and external specialists across multiple domains, including data engineering, analytics modeling and visualization, and governance strategy.
Flexible delivery models allow organizations to scale expertise as analytics programs evolve.
Data analytics services enable organizations to transform fragmented datasets into structured insight environments that support strategic decision-making and operational optimization.
Understanding both the benefits and implementation challenges helps organizations plan analytics initiatives more effectively, thereby improving long-term outcomes.
Benefits and Outcomes | Challenges |
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Improved Decision-Making: Enable faster, more accurate decisions by leveraging real-time and historical data insights.
Operational Efficiency Gains: Reduce costs and streamline processes by identifying inefficiencies and automation opportunities.
Enhanced Customer Experiences: Personalize interactions and improve satisfaction by analyzing customer behavior and preferences.
Revenue Growth Opportunities: Identify new revenue streams and optimize pricing, targeting, and product strategies.
Competitive Advantage: Strengthen market positioning by uncovering trends and acting on insights ahead of competitors.
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Data Silos and Fragmentation: Disconnected systems prevent a unified view of data across the organization.
Poor Data Quality: Inaccurate or inconsistent data undermines trust in analytics outputs.
Talent and Skill Gaps: Limited access to experienced data professionals slows implementation and impact.
Integration Complexity: Combining legacy systems with modern platforms introduces technical challenges.
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Data analytics services help organizations make more informed decisions and gain visibility across the business, enabling meaningful improvements in areas such as forecasting and performance monitoring.
These initiatives are widely used across industries to enhance forecasting accuracy, improve performance tracking, and drive innovation.
Advanced analytics techniques strengthen forecasting accuracy while supporting optimization strategies across operational environments.
Machine learning and predictive modeling capabilities enable organizations to anticipate customer behavior trends and evaluate and optimize operational decisions.
Advanced analytics environments often support transformation initiatives such as product strategy development and M&A Consulting performance benchmarking programs.
Business intelligence platforms provide structured visibility across performance indicators through:
These capabilities strengthen decision-making across both operational and strategic planning contexts.
Organizations increasingly generate value directly from their data assets through monetization initiatives that support enhanced digital products, new service offerings, and partner analytics ecosystems.
A well-structured analytics environment can turn internal data into a revenue stream, helping fund new and innovative projects.
Industry-aligned analytics solutions address sector-specific regulatory requirements and operational constraints across environments such as healthcare analytics, financial reporting, and retail demand forecasting platforms.
Tailored implementation strategies ensure analytics outputs remain aligned with industry expectations while supporting measurable business impact.
Data analytics services enable organizations to unlock the full value of their information assets by aligning insight generation with operational workflows and strategic planning priorities.
Well-designed analytics environments improve forecasting accuracy, strengthen performance visibility, and support innovation across digital initiatives.
Structured consulting support ensures analytics capabilities remain scalable, reliable, and aligned with long-term transformation objectives—helping organizations build sustainable competitive advantage through insight-driven decision-making.
Maximize your business performance with Toptal’s Data Analytics Services
Get a Free Consultation Now
