About Manifest Global

Manifest Global is building the infrastructure for global human capital mobility — connecting students, schools, universities, and employers across 50+ countries. Our portfolio spans Cialfo (AI-powered college counseling, 2,000+ schools), BridgeU (university guidance for international schools globally), Kaaiser (trusted study abroad counseling across India and Southeast Asia), and Explore (AI-powered university outreach, 1,000+ university partners). Together, we move talent across borders at scale. $80M raised. Still early.

About the Role

Cialfo's University Data Engineering team is the data backbone of everything students see on the platform — university profiles, course listings, entry requirements, fees, deadlines, rankings, and scholarship information across 544 partner universities and thousands more. Every piece of that data has to be collected, validated, and kept current.

We are hiring a Data Automation Engineer to own the automation function end-to-end — building the scrapers, AI-powered workflows, and data pipelines that replace manual data collection with reliable, production-grade automation. You will report to Engineering and work alongside the University Data Engineering team as the sole owner of the technical stack.

What makes this role different from a standard data engineering role: You will not be maintaining someone else's pipelines. You are building the function from scratch. The team has deep domain knowledge — they know what correct university data looks like, and they will QC your output. You bring the technical capability they do not have. Together, you replace hours of manual work per week. Your work ships directly to a product used by hundreds of thousands of students making university decisions.

What You Will Build

Your first 90 days have a defined backlog. The first priority is a notification classification pipeline to handle 450 alerts per week, replacing 6 hours of daily manual signal vs. noise triage across the team. Closely behind that is a signal addressal workflow covering 150 signals per week, replacing 6 hours of daily core updates — research, format, verify, push. You will also build an automated quality audit agent that runs nightly across all recent updates, replacing 6–7 hours of daily manual data accuracy checks. Beyond that, you will own rankings and key stats ingestion across 4,441 universities, replacing the full manual collection cycle for QS, THE, and US News rankings, as well as entry requirements extraction from dynamic JavaScript-rendered pages across 150+ universities.

Beyond the initial backlog, you own the full 25-task automation portfolio — maintaining what is already built, extending scrapers when source sites change structure, and designing new automations as the team's data commitments grow.

What You Own

• You own the complete automation stack — N8N workflows, Python scripts, LLM-powered extraction pipelines, and web scrapers. You build it, you maintain it, you fix it when it breaks.
• Production reliability is yours. When a scraper fails silently because a university website restructured, you diagnose and fix it without waiting to be told. You have monitoring in place so you know before the team does.
• AI output accuracy is your responsibility. The notification classifier, the quality audit agent, and any LLM-powered pipeline you build must meet defined accuracy gates. You own the test sets, the iteration, and the decision to go live.
• You make the data pipeline architecture decisions — N8N vs. Python, Firecrawl vs. Playwright, Claude API vs. rule-based extraction. You explain your reasoning; you do not wait for someone to decide for you.
• Working within Engineering, you will own direct API write access to Core, Contentful, and Explore for approved data types, removing the manual ticket loop for routine data pushes.

What You Bring

Non-Negotiable

• Python. Production-grade scripts you have owned — not scripts you contributed to. You wrote them, you ran them, you fixed them when they broke in production.
• REST API integration. You have built and maintained API clients against real production systems with rate limits, auth, pagination, and error handling. Not tutorial projects.
• Web scraping. Dynamic pages, JavaScript-rendered content, sites that block scrapers. You have handled all of these. You have rebuilt scrapers when source sites changed structure.
• Independent ownership. You have owned an automation or data pipeline without a senior engineer making the architecture decisions above you. You have been the person others came to when something broke.

Strong Preference

We have a strong preference for candidates with LLM API usage in production — you have shipped something where Claude or OpenAI was doing real work (classification, extraction, structured output) and you have dealt with the accuracy and reliability problems that come with it. We are also looking for 4–6 years of relevant experience, since independent ownership in messy production environments takes time to develop. Familiarity with N8N or equivalent workflow automation is a plus — you should be able to read, edit, and build N8N workflows without a tutorial, though it does not need to be your primary tool. Experience with unstructured, inconsistent source data — PDFs, scraped HTML, university websites with no consistency across 500 sources — is highly relevant.

Nice to Have

EdTech or university data domain knowledge is a plus, though the team teaches this faster than any candidate will self-learn it. SQL is useful for validation queries but is not required on day one.

What Good Looks Like at 90 Days

The notification classification pipeline is live and saving the team 20+ hours per week. You shipped it, it is in production, it has monitoring. You have diagnosed and fixed at least one automation that broke in production without asking Engineering for help. The team comes to you with data collection problems, and you come back with working solutions — not questions about how to approach them. You have made at least one tooling decision that changed how the team operates, and you can explain clearly why you made it. University Data Engineering Leads trust your QC gates without checking every output. Your accuracy track record has earned that.

Why This Role, Why Now

Cialfo serves hundreds of thousands of students making one of the most important decisions of their lives. The quality of the data on the platform directly affects what universities they see, whether application deadlines are accurate, and whether the fees they are planning around are correct.

The team has deep domain knowledge and operational discipline. What it does not have is the technical capability to automate the work that should not be manual. You are that capability — not a support function, but the reason the team can operate at a scale it currently cannot.

The work is real production automation problems: scrapers covering 4,441 universities, AI classifiers handling 450 alerts per week, quality agents running nightly across every recent data update. The team knows the domain deeply and will QC your work honestly. When something is wrong, you will hear it — that is a feature, not a bug. Every automation you ship converts manual hours into expanded data coverage — more universities, more countries, more students.

About Manifest Global

Manifest Global is building the infrastructure for global human capital mobility - connecting students, schools, universities, and employers across 50+ countries. Our portfolio spans Cialfo (AI-powered college counseling, 2,000+ schools), BridgeU (university guidance for international schools globally), Kaaiser (trusted study abroad counseling across India and Southeast Asia), and Explore (AI-powered university outreach, 1,000+ university partners). Together, we move talent across borders at scale. $80M raised. Still early.

About This Role

Most frontend roles ask you to ship features. This one asks you to ship features and build the system that lets QEs, PMs, and designers ship them too - safely, independently, with AI as the connective tissue. The Manifest engineering team already runs on AI-assisted workflows. The infrastructure exists in pieces. You'll complete it, maintain it, and raise the ceiling on what each function can do without engineering in the loop. AI is not the tool you use on the side. It is the medium you work in, and the capability you extend to everyone around you.

If you're early in your career and the most interesting question you have right now is "how do I build leverage for a team, not just output for myself?" - keep reading.

What will you own

You own the frontend - not just the code you write, but the system that lets everyone around you contribute to it. That means building features, and it means building the workflows, conventions, and guardrails that let QEs test independently, PMs contribute safely, and designers move from design to something real without waiting in a queue.

The measure of success here is not your personal output. It is how much the team around you can do because of what you built.

Concretely, that looks like:

• The shared AI conventions that keep tooling output consistent across the codebase, whoever is driving
• A skills library that encodes Angular patterns, API contracts, and business logic into workflows anyone on the team can invoke
• Safe zones and guardrails that let non-engineers contribute without creating engineering cleanup
• Design-to-code workflows that unblock designers without dev involvement
• A feedback loop with QEs, PMs, and designers - you hear where things break and you fix them

What success looks like

30 days. You've mapped the frontend architecture and the current cross-functional AI workflows. You know where the codebase is strong, where debt sits, and where QEs, PMs, and designers are getting stuck or falling back on engineering. You have a point of view on what to fix first.

90 days. The skills library has grown. QEs are generating test scaffolds from specs without filing requests to engineering. PMs are making frontend contributions inside defined safe zones with confidence. Designers have a clearer path from Figma to something testable. AI tooling output across the team is more consistent because of conventions you put in place.

6 months. Cross-functional AI workflows are a genuine productivity multiplier - not a set of experiments. Feature cycles are shorter because more of the team can move through more of the process independently. The frontend itself is faster, more reliable, and better structured. Saige and Explore's AI surfaces feel like first-class product experiences, not features the frontend barely holds together. The engineers, QEs, PMs, and designers working in this system are glad you built it the way you did.

About You

Qualifications

• Bachelor's degree in Computer Science, Engineering, or a related field - or equivalent practical experience

Experience

• 1–3 years of hands-on Angular experience - real projects you've built and shipped, even if not at large scale
• Solid TypeScript fundamentals and genuine curiosity about how the browser works as a runtime
• Active fluency with AI-assisted coding tools in your day-to-day work - you use them seriously, not occasionally
• The instinct to think about how your work affects the people around you, not just the ticket in front of you
• A communicator who can hold a useful conversation with a designer, a QE, or a PM - and follow through without being chased

Strong nice-to-haves

• Any exposure to AI-facing UIs - streaming output, recommendation surfaces, or agent interfaces - even in side projects
• Experience or genuine interest in developer experience: documentation, shared patterns, making codebases easier for others to work in
• Early signs of cross-functional thinking - instances where you made something easier for a non-engineer, even informally

Why Manifest

We're building the infrastructure for global human capital mobility - the rails that move students, schools, universities, and employers across 50+ countries. Cialfo is in 2,000+ schools. Explore is trusted by 1,000+ universities. BridgeU runs across the UK, Europe, and the Middle East. Kaaiser has guided students across India and Southeast Asia since 1997.

The opportunity is real. $700B flows annually in remittances from migrant workers. 85M workers will be missing from developed economies by 2030. We're building the operating system that changes that.

$80M raised from Tiger Global, SIG, and Square Peg. Still early.

The team has already built the infrastructure for AI-native engineering - shared conventions, a live skills library, AI-assisted workflows across engineering, QE, product, and design. Saige is in production. Explore's AI capabilities are in production. This isn't an aspiration we're hiring you to bring to life. It's an operating system we're hiring you to extend, scale, and make permanent.

About Manifest Global

Manifest Global is building the infrastructure for global human capital mobility - connecting students, schools, universities, and employers across 50+ countries. Our portfolio spans Cialfo (AI-powered college counseling, 2,000+ schools), BridgeU (university guidance for international schools globally), Kaaiser (trusted study abroad counseling across India and Southeast Asia), and Explore (AI-powered university outreach, 1,000+ university partners). Together, we move talent across borders at scale. $80M raised. Still early.

About This Role

Most backend roles ask you to ship endpoints and migrations. This one asks you to ship those and extend the platform that lets the rest of the engineering team - and the AI agents working alongside them - ship them too. That platform already exists. Shared conventions, the skills library, safe contribution zones, spec-to-endpoint workflows, AI-agent infrastructure powering Saige and Explore - all live, all in production, all being used today. You'll own its next chapter: deepening it, scaling it, and raising the ceiling on what each engineer can do without re-deriving the same patterns.

If the most interesting question in your career right now is "how do I build leverage for a team, not just output for myself?" - keep reading.

What will you own

You own the backend - not just the code you write, but the system that lets everyone around you contribute to it. That means shipping production Rails services, designing APIs other teams build against, and owning the data layer end to end. And it means extending the workflows, conventions, and guardrails that let QEs generate test scaffolds independently, PMs and analysts ship config and feature-flag changes safely, and AI agents operate inside the codebase reliably.

The measure of success here is not your personal output. It is how much the team around you can do because of what you built.

Concretely, that looks like:

• Shared AI conventions that keep tooling output consistent across the codebase - for backend code generation, schema changes, and migrations - whoever or whatever is driving
• A backend skills library that encodes Rails patterns, service contracts, domain models, and query patterns into workflows anyone on the team can invoke
• Safe zones and guardrails that let PMs and analysts ship config changes, feature flags, and scoped data changes without creating engineering cleanup
• Spec-to-endpoint and ticket-to-migration workflows that compress the path from intent to working backend code
• AI-agent backend infrastructure - runtimes, tool-calling layers, retrieval pipelines, evals - that powers Saige, Explore, and what comes next, and scales with each new feature
• A feedback loop with engineers, QEs, and PMs building against the platform every day - you hear where things break and you fix them

What success looks like

• 30 days. You've mapped the existing platform, the conventions, the skills library, and the AI workflows the team is already using. You know where they're sharpest, where they're starting to bend under load, and where the next wave of investment should go.
• 90 days. You've shipped meaningful additions to the skills library, tightened conventions where they were drifting, and closed gaps the team has been working around. Other engineers are reaching for your work instead of writing from scratch.
• 6 months. The backend platform is materially more capable than when you joined. Saige and Explore's AI surfaces hold up under heavier production load because of infrastructure decisions you made. PMs and analysts are shipping more inside safe zones, with less cleanup. The engineers and agents building against the platform move faster - and they know why.

About You

Qualifications

• Bachelor's degree in Computer Science, Engineering, or a related field - or equivalent practical experience

Experience

• 5+ years of backend engineering experience on production systems
• Strong production Rails experience - services you've owned and shipped, not contributed to at the margins
• Real fluency with PostgreSQL or MySQL, including query optimization at scale
• Experience designing and maintaining RESTful APIs that other teams build against
• Daily fluency with AI-assisted coding tools (Claude Code, Cursor, Codex, or similar) - and a track record of making them work consistently for other people, not just yourself
• The instinct to treat a non-engineer touching your platform as a capability to design for, not a risk to manage

Strong nice-to-haves

• Hands-on AI/LLM backend work - retrieval pipelines, agent runtimes, tool-calling, evals, prompt versioning
• Developer experience or platform engineering background - internal SDKs, shared libraries, opinionated documentation
• Cloud experience (AWS, Azure, GCP) and serious testing discipline (RSpec, Capybara)
• Evidence of cross-functional thinking - something you built that made a non-engineer's life easier

Why Manifest

We're building the infrastructure for global human capital mobility - the rails that move students, schools, universities, and employers across 50+ countries. Cialfo is in 2,000+ schools. Explore is trusted by 1,000+ universities. BridgeU runs across the UK, Europe, and the Middle East. Kaaiser has guided students across India and Southeast Asia since 1997.

The opportunity is real. $700B flows annually in remittances from migrant workers. 85M workers will be missing from developed economies by 2030. We're building the operating system that changes that.

$80M raised from Tiger Global, SIG, and Square Peg. Still early.

The team has already built the infrastructure for AI-native engineering - shared conventions, a live skills library, AI-assisted workflows across engineering, QE, product, and design. Saige is in production. Explore's AI capabilities are in production. This isn't an aspiration we're hiring you to bring to life. It's an operating system we're hiring you to extend, scale, and make permanent.

About Manifest Global

Manifest Global is building the infrastructure for global human capital mobility -connecting students, schools, universities, and employers across 50+ countries. Our portfolio spans Cialfo (AI-powered college counseling, 2,000+ schools), BridgeU (university guidance for international schools globally), Kaaiser (trusted study abroad counseling across India and Southeast Asia), and Explore (AI-powered university outreach, 1,000+ university partners). Together, we move talent across borders at scale. $80M raised. Still early.

About This Role

Manifest Global operates four brands across 50+ countries, generating data across thousands of schools, hundreds of thousands of students, and 1,000+ university partners. Counselor behaviour, student application journeys, university conversion rates, placement outcomes, attribution revenue - it's all there. The data exists. The question is whether the infrastructure around it is good enough to make it useful.

Right now, the data platform works. Pipelines run, the warehouse holds data, the BI layer surfaces reports. But Manifest is growing - new brands, new markets, new activation use cases - and the infrastructure needs to scale with it. There are pipelines that need to be more reliable. Transformation logic that needs to be cleaner. Warehouse design that needs to handle more volume without degrading performance. And an activation layer - reverse ETL, operational analytics, data flowing into the tools the business actually uses - that is still being built.

As a Senior Data Engineer, you will own significant parts of the data platform end to end - ingestion, transformation, warehouse, activation - and you will be one of the people who determines whether Manifest's data infrastructure is a genuine competitive advantage or a persistent constraint. You will work closely with Principal Engineers, Product, and business stakeholders across all four brands, and you will be expected to operate with the ownership and judgment of someone who has built production-grade data systems before.

What makes this role different: Manifest has real data - cross-brand, multi-geography, commercially significant data. The stack is modern: Snowflake, dbt, Hevo, Airtable, Metabase. The problems are real. And when the data infrastructure surfaces the right insight, it changes a decision that affects real students and real institutions.

AI is central to how we build: This isn't just a data engineering role - it is a role where you will actively design and build AI infrastructure that accelerates the team's own development velocity. We use Snowflake Cortex AI with Claude in our daily engineering workflow - for debugging, RCA, query optimisation, and pipeline analysis. We have already cut root cause analysis time. The next step is embedding AI deeper: automated ticket handling, intelligent monitoring, and AI-assisted development tooling that lets the team move faster without sacrificing reliability.

What You Will Own

1. AI Infrastructure for Data Engineering

• Design and build AI-assisted development tooling - LLM-powered code generation for dbt models, SQL transformations, and pipeline scaffolding that dramatically reduces time-to-production for new data assets
• Build intelligent data quality and anomaly detection systems - AI-driven monitoring that learns normal patterns across pipelines and surfaces anomalies before they propagate downstream, replacing manual threshold-based alerting
• Implement AI-augmented data cataloguing and lineage - automated documentation generation, schema understanding, and semantic tagging so engineers spend less time writing docs and more time building
• Develop AI-powered pipeline debugging and root cause analysis - tooling that diagnoses failures, traces impact through the DAG, and proposes fixes rather than requiring engineers to trace failures manually
• Build and maintain the infrastructure that supports AI features - vector stores, embedding pipelines, retrieval layers, and model serving infrastructure that powers AI capabilities across Cialfo, BridgeU, and Explore
• Evaluate and adopt emerging AI developer tools - stay ahead of how AI tooling (Claude, Cortex AI, GitHub Copilot, LLM APIs) can be embedded into the team's workflow to shorten feedback loops and accelerate feature delivery

2. Data Warehouse Design, Cost & Maintenance

• Own significant portions of the Snowflake data warehouse - schema design, performance optimisation, and the integrity of the data models that the rest of the stack depends on
• Apply strong data warehousing methodologies: dimensional modelling, layered transformation logic, clear separation between raw, staged, and served layers
• Design and build cross-brand data primitives - shared, canonical data layers for K12, Student, University, and Application data that work consistently across Cialfo, BridgeU, and Kaaiser. This is active work and a critical foundation for the multi-brand data platform
• Own Snowflake cost optimisation - monitor warehouse spend, identify high-cost queries and sync jobs, right-size warehouse configurations, and drive measurable reductions in monthly compute spend.
• Ensure the warehouse handles increasing data volumes from across all four brands without degrading query performance or downstream reliability

3. ETL/ELT Pipelines, Scheduling & Transformation Logic

• Design, build, and maintain production-grade data pipelines - ingestion via Hevo or similar, transformation via dbt, SQL-based logic that is clean, documented, and maintainable
• Design and manage Snowflake Task DAGs - build and maintain dependency-chained task graphs for ingestion, LLM processing, and sync workflows. Understand how to structure root tasks, child tasks, CRON scheduling, warehouse assignment, and failure isolation so pipelines don't cascade-fail
• Own Airtable as an operational data layer - manage Snowflake-to-Airtable and Airtable-to-Snowflake sync workflows, including the SNOWFLAKE_TO_AIRTABLE_TABLELIST sync config, upsert logic, incremental filters, and sync cost optimisation. Airtable is both a key data source and a reporting destination across brands
• Build and own reverse ETL workflows that activate warehouse data into operational tools - getting the right data into the hands of the teams that need it, not just into dashboards
• Take full ownership of pipeline failures: root cause identification, fix, downstream impact analysis, and prevention - not just resolution

4. Data Quality & Reliability

• Define and enforce data quality standards across datasets you own - automated validations, delta checks, row counts, time-based monitoring
• Build monitoring and alerting that surfaces problems before they reach the business
• Document data lineage, transformation assumptions, and technical decisions so the platform is understandable and maintainable as the team grows

5. BI Platform & Reporting

• Maintain and enhance the existing BI layer - Metabase and build reporting interfaces that non-technical stakeholders can actually use
• Collaborate with Product and Analytics teams to translate business needs into reliable technical solutions
• Communicate clearly across all four brands - be the person who can explain a data problem in business terms and a business problem in data terms

What Success Looks Like

You'll start by building a complete picture of the current state - which pipelines are fragile, where data quality is inconsistent, what the highest-impact improvements look like, and where activation use cases aren't yet built. You will have a point of view on where to move first.

From there, the infrastructure will be measurably more reliable. Pipelines that were breaking will run consistently. Data quality issues will be caught early or prevented entirely. The BI layer will be getting used - not just maintained.

Over time, the data platform will be something the business genuinely relies on - fast enough to support the pace of growth, reliable enough that data consumers trust what they are looking at, and built in a way that the next engineer who joins can understand and extend without starting from scratch.

About You

Qualifications

• Bachelor's degree in Computer Science, Engineering, or a related field - or equivalent practical experience

Experience

• 5+ years building and maintaining production-grade data pipelines and data warehouses - not prototypes, but systems that real business decisions depend on
• Strong experience architecting and implementing large-scale business intelligence solutions
• Strong experience in data warehouse design and advanced SQL
• Hands-on experience with the modern data stack: Snowflake (or equivalent cloud warehouse), dbt, Hevo or Airbyte or similar
• Advanced SQL proficiency: CTEs, window functions, QUALIFY, query optimisation, and understanding the difference between SQL that works and SQL that scales
• Experience with Snowflake-native features: Task DAGs, Dynamic Tables, Snowflake Cortex AI, AI_COMPLETE, warehouse sizing, and query profiling
• Experience with Airtable as an operational data layer - syncing data between Snowflake and Airtable, managing upsert logic, and keeping sync costs low
• Experience owning Snowflake cost monitoring and optimisation - identifying expensive queries, bloated sync jobs, and warehouse over-provisioning
• Experience with user-facing BI tools: Metabase, Looker, or similar
• Experience working in agile, ticket-based workflows (Jira)
• Experience building or integrating AI/LLM tooling into data engineering workflows - specifically Snowflake Cortex AI, or LLM APIs (OpenAI, Anthropic, etc.) for structured data tasks, fuzzy matching, or pipeline automation
• Familiarity with vector databases, embedding pipelines, or retrieval-augmented generation (RAG) infrastructure is a strong plus
• Exposure to MLOps or AI infrastructure patterns - model serving, feature stores, or AI monitoring - is an advantage

Skills & Qualities

• You take ownership of reliability. When a pipeline fails, you don't just fix the immediate problem - you understand why it happened, what it affected downstream, and what needs to change so it doesn't happen again
• You communicate clearly with non-technical stakeholders. You've sat in a requirements conversation with a commercial or product team, understood what they were actually asking for, and built something that answered the real question
• You document your work well enough that someone else could maintain it - data lineage, transformation assumptions, technical decisions
• Comfortable working in a multi-brand, multi-stakeholder environment where data problems span different systems, teams, and geographies
• Strong problem-solving skills and attention to detail
• Ability to hold multiple priorities simultaneously and make good judgments about what to work on first
• You're genuinely excited about AI as a force multiplier for engineering teams - you actively use AI tools in your own workflow and have a point of view on how they can make data teams faster without sacrificing reliability

Why Manifest

We're building the infrastructure for global human capital mobility - the rails that move students, schools, universities, and employers across 50+ countries. Cialfo is in 2,000+ schools. Explore is trusted by 1,000+ universities. BridgeU runs across the UK, Europe, and the Middle East. Kaaiser has guided students across India and Southeast Asia since 1997.

The opportunity is real. $700B flows annually in remittances from migrant workers. 85M workers will be missing from developed economies by 2030. We're building the operating system that changes that.

$80M raised from Tiger Global, SIG, and Square Peg. Still early.

The team has already built the infrastructure for AI-native engineering - shared conventions, a live skills library, AI-assisted workflows across engineering, QE, product, and design. Saige is in production. Explore's AI capabilities are in production. This isn't an aspiration we're hiring you to bring to life. It's an operating system we're hiring you to extend, scale, and make permanent.