ScrumLaunch

07/09/2026

#428 Data QA Analyst (LatAm)

We're hiring a Data QA Analyst for our Latin America team to improve accuracy and consistency of data systems. In this role, you'll perform root cause analysis, build data visualizations to support workflows, optimize data pipelines with Engineering teams, and identify automation opportunities. If you have 1+ years of QA analysis experience, strong SQL and Python skills, and familiarity with data warehousing solutions like Snowflake or BigQuery, check more details below and let's connect!
Responsibilities & Skills:
◾️ Conduct QA procedures to identify opportunities to improve data accuracy and consistency
◾️ Work closely with Engineering teams to refine systems and pipelines, ensuring data flows smoothly from source to destination
◾️ Perform root cause analysis using SQL and Python to uncover nuances and issues in the data
◾️ Build data visualizations to support you and your teammates in day-to-day QA activities
◾️ Identify opportunities for process improvement through automation and work with Engineering teams to build automated solutions
◾️ Conduct regular backtesting processes and present QA findings to internal and external stakeholders
◾️ Clean and normalize key data sources using SQL and Python
◾️ Communicate updates and progress to Product, Analytics, and other stakeholders, ensuring alignment on priorities and timelines
◾️ Maintain a strong sense of ownership over your work and demonstrate strong organizational skills
◾️ Participate in team meetings, project planning, and knowledge-sharing activities
◾️ Adopt a self-starter mentality and inspire other team members to do the same
Experience:
Must-have:
◾️ Bachelor’s degree in a technical field such as Industrial Engineering, Information Systems, or a related field
◾️ 1–3 years of experience as a QA Analyst or in a similar role
◾️ Proficiency in SQL
◾️ Experience with Python for data analysis and validation
◾️ Familiarity with data warehousing solutions such as Snowflake, Google BigQuery, or ClickHouse
◾️ Strong analytical and problem-solving skills
◾️ Strong attention to detail and commitment to delivering high-quality work
◾️ Clear written and verbal communication skills
◾️ Ability to prioritize tasks and manage time effectively in a fast-paced environment
◾️ Ability to collaborate effectively with technical and non-technical stakeholders
Nice to have:
◾️ Experience building and using data visualization tools such as Hex, Preset, or Tableau
◾️ Understanding of statistical measures such as correlation and variance
◾️ Experience identifying and implementing operational improvements through automation
Please note that the hiring process includes a test assignment.
Language proficiency:
◾️ English – Upper-Intermediate (B2)
We offer:
◾️ Opportunity to grow as an expert
◾️ Flexible working conditions
◾️ 12 days of paid vacation
◾️ 5 sick days and off days
◾️ Friendly and open-minded team
◾️ English classes
◾️ Access to psychologist support for mental well-being
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👇 Apply Now:
https://cutt.ly/sylut3RM
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We’re looking for a **Data QA Analyst** with strong SQL skills and a sharp eye for data quality to join our team. This role is ideal for someone who enjoys digging into data, identifying inconsistencies, understanding why they happen, and working closely with Engineering and Analytics teams to imp...

20/08/2026

You built the product. Now how do you get someone to pay for it?
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Your first 10–20 B2B SaaS customers usually won't come from waiting for inbound or automating cold email. They come from founders finding the right people, talking to them, and learning what makes them buy.
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Here's how to find those first customers and when to turn manual outreach into a system: https://www.scrumlaunch.com/blog/how-to-find-first-customers-2026 .⠀⠀
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19/08/2026

Remote vs. In-House Teams: What’s the Best Fit for Your Tech Startup?
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Your startup is growing fast - new customers, new features, bigger goals. Exciting, right? But with growth comes a question almost every founder faces: how do I build the right team to keep up? Should you go traditional in-house, or embrace a remote setup? Let’s discuss it! 👇
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In-house teams feel familiar. Everyone’s in the same space, using the same tools, attending the same meetings. Communication is instant. You can see progress, give feedback, and mentor people directly. Culture forms naturally - coffee breaks, shared lunches. But it comes with costs: rent, utilities, equipment, and office space. Plus, your talent pool is limited by location, and you’re competing with tech giants in major hubs.
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Remote teams flip the script. Suddenly, you can hire the best developer in Berlin, a designer in Bangalore, a PM in Buenos Aires. Costs are lower, scaling is faster, and your hiring funnel expands globally. But remote work demands discipline. Lack of face-to-face communication can lead to misunderstandings. Time differences can slow down decision-making. A company's culture needs to be actively nurtured. Without it, your agile team can get fractured.
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So how do you choose when neither option is perfect? Consider:
• Stage of your startup: Early-stage, bootstrapped companies often go remote to stay lean and move fast.
• Type of work: Hardware, sensitive data, or highly collaborative sessions usually benefit from in-person interaction.
• Speed vs. cohesion: Need people contributing yesterday? Remote works. Want strong culture and trust at scale? In-house or hybrid wins.
• Values & priorities: If you value global diversity and flexibility, remote fits. Want a tight-knit team aligned deeply? In-house is better.
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💡 Which approach fits your startup right now?
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🔗 Check our blog for a detailed breakdown and practical tips for deciding the right model for your startup: https://www.scrumlaunch.com/blog/remote-vs-in-house-teams-best-fit-for-tech-startup
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ScrumLaunch enables start-ups, businesses, and organizations to hire developers from a growing network of top talent in the world.

17/08/2026

AI Voice Agents are changing customer interactions, and we’re looking for a Product Manager/Product Owner with 3+ years of B2B SaaS experience to help shape one. In this role, you'll own the product roadmap, coordinate two engineering teams, translate requirements into clear Linear tickets, and make day-to-day product decisions independently. If you're experienced with Linear, comfortable working with technical specs and Figma, and can manage ambiguity across time zones, check out the details below and apply!

Requirements:

Hard Skills

◾️3+ years of product management experience on a B2B SaaS product;

◾️Experience writing PRDs, user journeys, and acceptance criteria;

◾️Proficiency with Linear (or similar: Jira, Shortcut) for backlog and sprint management;

◾️Experience coordinating multiple engineering teams working on a shared product: defining ownership, managing handoffs, and resolving cross-team dependencies;

◾️Comfortable reading technical documentation and API specs: you don't need to code, but you need to understand what engineers are building and why;

◾️Experience working with design tools (Figma) and reviewing UI implementations against specs;

◾️Familiarity with AI/ML products, conversational AI, or voice/telephony systems is a strong plus;

◾️Tools: Linear, Notion, Figma, Slack, GitHub.

Soft Skills

◾️Excellent written communication;

◾️Ability to manage ambiguity;

◾️Strong stakeholder management;

◾️Proactive communication — surface risks, blockers, and trade-offs before they become problems;

◾️Attention to detail;

◾️Comfortable working asynchronously across time zones.

Responsibilities:

◾️Own the product roadmap, PRDs, and feature scope for the AI Voice Agent;

◾️Translate client requirements and design feedback into clear, actionable Linear tickets with acceptance criteria;

◾️Organize and manage the process for two development teams working side by side, define ownership boundaries, coordinate dependencies, and ensure both teams have clarity on scope and priorities;

◾️Lead cycle planning: define priorities, scope stories, estimate with engineering, and confirm assignments across both teams;

◾️Facilitate demos, syncs, status updates, and retrospectives; surface blockers early;

◾️Maintain and evolve product documentation (PRDs, template defaults, decision logs, backlog);

◾️Make day-to-day product decisions without waiting for committee approval.

Please note that the hiring process includes a test assignment.

Language proficiency:

◾️English – Advanced (C1)

We offer:

◾️Opportunity to grow as an expert;

◾️Flexible working conditions;

◾️12 days of paid vacation;

◾️5 sick days and off days;

◾️Friendly and open-minded team;

◾️English classes

◾️Access to psychologist support for mental well-being.

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👇 Apply Now:

https://cutt.ly/Zys7vRez

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14/08/2026

When you're launching an online store, Shopify is usually the obvious choice. It's fast, affordable, and lets you start selling without hiring a development team. Hosting, security, payments, and checkout are all handled out of the box.
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For most businesses, that's exactly what they need.
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But it is a mistake to assume Shopify will always be the right answer as the business evolves. A few common signals you've outgrown Shopify:
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🔹 You're stitching together multiple apps just to make a single workflow function. Each app works individually, but the overall system becomes increasingly fragile.
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🔹 Your app spend keeps climbing. What started as a few subscriptions has turned into a meaningful monthly operating expense.
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🔹 Your developers spend more time working around platform constraints than building new capabilities.
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🔹 You've looked at Shopify Plus, and your core problem still isn't solved. Sometimes the issue isn't the plan you're on. It's the platform itself.
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🔹 Your business model doesn't cleanly align with Shopify's assumptions. Complex B2B pricing, approval workflows, rentals, marketplaces, or highly customized customer journeys often require more workarounds than the platform was designed for.
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What many businesses miss is that the choice isn't always Shopify or a complete rebuild.
In practice, there is a large middle ground. Some companies keep Shopify as their commerce engine while building custom frontend experiences. Others keep the storefront and checkout while moving complex business logic into custom backend systems. Some do both.
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💡 The key question isn't "Is Shopify good enough?" It's "Is Shopify helping your business grow, or is your business adapting itself to Shopify?"
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For most startups and SMBs, Shopify remains one of the best ways to launch and grow.
But when platform constraints start shaping your roadmap more than your strategy, it's probably time to rethink the architecture.
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Have you ever hit Shopify's limits?
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Read our latest blog post for a deeper look at when Shopify stops being the right fit at:
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14/08/2026

Cowork. ChatGPT Work. Now Grokbot. In the span of a few months, every major AI lab has shipped some version of the same idea: a persistent, cloud-based agent that keeps working even after you close the laptop. So what's left to fight over?
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Turns out, quite a lot, because how you talk to that agent matters just as much as what it can do.
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Grokbot's answer is the most different one yet, and it might be a more interesting story than the underlying tech. Read more here: https://www.scrumlaunch.com/blog/grok-bot-xai-features-capabilities-ai-assistance .⠀
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12/08/2026

OpenAI has launched Presence, but it's more than just another AI product. Why does it matter, and what does it actually mean for businesses?
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It signals a shift from building smarter models to helping companies adopt AI agents that are reliable enough for real enterprise workflows.
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Here's what Presence is, how it works, and why this launch matters: https://www.scrumlaunch.com/blog/openai-presence-features-business-use-cases-benefits .
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11/08/2026

#423 Data Operations Analyst (Ukraine)

We're hiring a Data Operations Analyst for our Ukraine-based team! In this role, you’ll perform root cause analysis, conduct QA procedures, build automated solutions with Engineering teams, and create data visualizations to support workflows. If you have 1+ years of experience in data analysis or QA, strong SQL skills, and familiarity with data warehousing solutions like Snowflake or BigQuery, this position could be your perfect fit.
Requirements:
◾️ Bachelor's degree in a technical field (e.g., Industrial Engineering, Information Systems) or equivalent practical experience in a data-heavy environment
◾️ 1–3 years of experience as a QA Analyst, Data Analyst, or similar role
◾️ Strong proficiency in SQL and familiarity with data warehousing solutions (e.g., Snowflake, Google BigQuery, ClickHouse)
◾️ Experience cleaning and normalizing key data sources using SQL and Python
◾️ Experience building and using data visualization tools (e.g., Hex, Preset, Tableau) is a plus
◾️ Understanding of statistical measurements of accuracy (e.g., correlation, variance)
◾️ Experience identifying and executing operational improvements through automation
Who You Are (Attributes):
◾️ An Owner: You maintain an attitude of ownership of your work and adopt strong organizational skills
◾️ A Self-Starter: You adopt a self-starter mentality and inspire other team members to do the same
◾️ A Collaborator: You possess effective communication and collaboration skills, with the ability to work well in a team environment
◾️ A Clear Communicator: You are adept at clear, concise written and verbal communication, comfortable interacting with both technical and non-technical audiences
◾️ Resilient: You have the ability to prioritize tasks and manage time effectively in a fast-paced environment
Responsibilities:
◾️ Be the Data Detective: Perform root cause analysis using SQL and Python to uncover nuances and issues with the data, ensuring nothing slips through the cracks
◾️ Drive Quality Assurance: Conduct QA procedures and regular backtesting processes to pinpoint opportunities for enhancing data accuracy and consistency
◾️ Engineer Automated Solutions: Identify process improvements through automation and work hand-in-hand with Engineering teams to build automated solutions
◾️ Optimize Data Pipelines: Clean and normalize key data sources and collaborate with Engineering to refine systems, ensuring data flows smoothly from source to destination
◾️ Visualize Health: Build data visualizations that support you and your teammates’ day-to-day QA workflows
◾️ Communicate & Align: Communicate updates and progress to Product, Analytics, and other stakeholders, ensuring alignment on priorities and timelines
Please note that the hiring process includes a test assignment.
Language proficiency:
◾️ English – Upper-Intermediate (B2)
We offer:
◾️ Opportunity to grow as an expert
◾️ Flexible working conditions
◾️ 12 days of paid vacation
◾️ 5 sick days and off days
◾️ Friendly and open-minded team
◾️ English classes
◾️ Access to psychologist support for mental well-being
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👇 Apply Now:
https://cutt.ly/lyp7sKyD
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07/08/2026

🤖AI has quietly become part of almost every digital experience we have. Most of us interact with AI dozens of times a day without thinking much about it - recommendations, voice assistants, image editing, navigation, wearables, smart homes and so on.
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What many people also don't think about is where that AI actually runs.
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☁️ Right now, most AI experiences still depend on the cloud. Your device captures information, sends it to remote servers, receives the result, and then presents it back to you. For many use cases, this works extremely well.
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But there are categories of products where even small delays start creating problems. 🚗 Take autonomous vehicles as an example. It makes decisions constantly, and sending every calculation somewhere else for processing creates dependencies that aren't always acceptable.
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This is where on-device AI starts becoming important.
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The idea of On-device AI is simple - instead of moving data to AI, move AI closer to the data.
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And that changes a lot. First, speed. ⚡When AI runs locally, responses become almost instant. Voice assistants react faster, cameras enhance images in real time, and translation apps can work even without connection.
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Second, privacy. 🔒 Your data doesn't need to leave the device every time AI is used.
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Third, reliability. 📶 On-device AI continues working when connectivity doesn't. Navigation apps still guide users offline, smart home devices can keep responding, and wearables continue monitoring health information.
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What makes On-device AI possible is a combination of better hardware and optimized software. Devices now include CPUs, GPUs, and dedicated AI accelerators designed specifically for ML workloads. Techniques like quantization, pruning, and model compression reduce resource requirements without heavily affecting performance.
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Cloud AI isn't disappearing. But the direction is becoming clearer: AI is gradually moving closer to the user.
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Check out more insights on the topic at https://www.scrumlaunch.com/blog/on-device-ai-how-it-works-benefits-and-real-world-use-case.
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06/08/2026

Building an AI-powered product has never been easier. Companies can integrate LLMs, AI agents, and automation workflows faster than ever. But will it work when real users start using it?
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AI evaluation helps teams move beyond “it worked when we tried it” and measure what really matters: accuracy, relevance, safety, consistency, performance, and business impact.
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In our latest blog post at https://www.scrumlaunch.com/blog/ai-evaluation-framework , we explore what AI evaluation really means, which metrics matter, and how teams can build AI systems users can trust. Read the full article below.
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ScrumLaunch enables start-ups, businesses, and organizations to hire developers from a growing network of top talent in the world.