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Alex Denisov

Tableau Developer
Former senior web developer at AbacusNext.
Alex Denisov is an experienced web development professional specializing in frontend technologies, web applications, and data visualization. His technical expertise includes HTML5, CSS3, Sass/Compass, JavaScript, jQuery, Bootstrap, React, Angular, Backbone, D3, SVG, PhantomJS, Node.js, Java, PHP, WordPress, Genesis Framework, Drupal, Magento, Symfony, Spring, Ghost, Shopify, and Squarespace.

Nitish Maharjan

Tableau Developer
Skilled in healthcare data analysis, SQL, Amazon Redshift, PHP, PowerBI, & Tableau.
Nitish Maharjan is an experienced data engineer with a demonstrated history of working in the computer software industry. He is skilled in healthcare data analysis, SQL, Amazon Redshift, database, PHP, Android app development, Tableau, Power BI data visualization tools, Python for data science, and machine learning. His experience includes working as a data analyst for companies such as Pulp You Media, Zupply Pty Ltd, and Deerwalk Services Pvt Ltd.

Ernesto Suguer

Tableau Developer
Former data analyst at Morgan Stanley.
Ernesto Suguer is a data analyst with over 15 years of experience in business intelligence and analytics fields. He is the director of analytics at Reef Technology and former data scientist and business intelligence specialist at Airbnb Canada. At Morgan Stanley, he served as a data analyst who designed BI applications using Oracle Essbase and regulatory reports and templates using the Clarity visualization tool. Ernesto also worked for Expedia, Intel, and Future Electronics.

Heli Shah

Tableau Developer
Former data analyst at The Madison Square Garden Company.
Heli Shah is a solutions-driven data analyst with over four years of experience in leading cross-functional data in development, documentation, and delivery of process innovations driving the attainment of business goals. Her areas of expertise include statistical analysis, machine learning, data analytics, data visualization, data science research, data reporting, data mining and cleaning, and preparation. At The Madison Square Garden Company, she was responsible for the development and delivery of ad-hoc and scheduled reports and data sourced from the Oracle Fusion Human Capital Management system. Heli also worked as a data analyst at The New School in New York, where she created highly segmented reports and data extracts for development, alumni relations, and other departments throughout the university.
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Frequently asked questions

Most teams see matched candidates within 48 hours. Our pre-vetted network means we are not starting from scratch when you reach out. If we do not have the right person available, we will tell you upfront rather than scramble.
Practitioners vet practitioners. Your growth marketer candidate gets interviewed by someone who has run growth at similar companies, not a recruiter checking keywords. We combine technical assessments, portfolio reviews, and cultural fit screening before anyone reaches your inbox.
Contract talent works on defined projects with clear deliverables. Embedded talent integrates into your team for ongoing work, attending standups and using your tools. Both can transition to full-time if the fit is right. We help you choose based on scope, timeline, and budget.
If it is not working in the first few weeks, we will find you a replacement at no additional cost. We are not in the business of defending bad matches. Most clients never need this because we invest heavily in fit upfront.
We offer flexible engagement models: hourly for short-term projects, monthly retainers for ongoing work, or placement fees for full-time hires. No hidden fees, no long-term contracts unless you want them. Your account manager will walk through options on your discovery call.
Yes. Someone who thrived at Google with dedicated QA, DevOps, and product managers may flounder when they have to wear all three hats. We look for people who have operated at your stage, pace, and level of ambiguity.
Yes, and not just the flashy demo-building part. Our Agent Operations practice includes people who have run AI agents in production and dealt with the messy reality of keeping them reliable, cost-effective, and safe.
Most marketplaces optimize for volume. They send you 10 candidates hoping one sticks. We optimize for fit. We would rather say “we do not have the right person” than waste your time. Our 95% satisfaction rate comes from being selective, not fast.