data engineer

ai engineer

planning engineer

senior data engineer

ai & data engineer

data pipeline engineer

Seeka Technology

<div>Company Description 
We believe strong early career roles should do more than give someone a job title. They should help people grow into the kind of professionals they want to become. At SEEKA Technologies, we give junior team members meaningful work that builds real capability, sharpens problem-solving, and develops practical experience in fast-moving business and technology environments. Our goal is to help emerging talent strengthen their skills, expand their potential, and prepare for a future shaped by constant innovation across business and IT.
SEEKA Technologies (Not Seeka Limited) is a project under its parent organisation, called Fresh Futures Australia, which is an education consultant based in both Australia and Malaysia. We are developing and creating a platform that utilises A.I. to help match students and job seekers to the right opportunities relevant to them from Kindergarten up to the University, along with vocational training centres and language schools, and of course to businesses and companies who need the right candidates. Our mission is to make it easier for anyone to find, filter and apply to educational institutions and companies in a more seamless manner.
We are currently looking to hire a junior Backend AI &amp; Data Pipeline Engineer who wants to build real-world experience in backend systems, data processing, scraping, retrieval, and cloud-based infrastructure. This role is ideal for someone who already has hands-on technical experience and wants to grow further by working on meaningful engineering challenges that support Yuzee&rsquo;s intelligent matching platform. You will contribute to the systems that process data, power search and matching, and improve the efficiency, reliability, and scalability of our platform.
Below are the important details you will need to take note of:<ul><li>
English is the primary language used in the role</li><li>
This is a full-time remote/work-from-home position</li><li>
We welcome both local and international candidates</li><li>
Candidates should have a degree or proven practical experience relevant to the role</li></ul> 

Job Description 
About the role
We are looking for a Backend AI &amp; Data Pipeline Engineer to own the end-to-end data processing infrastructure that powers Yuzee's intelligent course and job matching platform. You will design and maintain scalable, event-driven pipelines that process tens of thousands of daily records, generate semantic embeddings, and feed a growing knowledge graph used for personalised career pathway recommendations.
What you'll do<ul><li>
Design and maintain three distinct processing pipelines &mdash; scheduled job ingestion, event-driven course processing, and a periodic knowledge graph builder &mdash; each with independent trigger logic and cost controls</li><li>
Generate and manage semantic embeddings via Amazon Bedrock (Titan v2), index them in MongoDB Atlas Vector Search, and calibrate similarity thresholds to ensure match accuracy</li><li>
Build and maintain a knowledge graph linking jobs, courses, skills, and industries using FP-Growth association rules and archetype-to-SOC code mapping</li><li>
Build and improve a two-stage discovery and matching API on AWS Lambda &mdash; vector retrieval first, then deep eligibility scoring with LLM re-ranking</li><li>
Right-size Fargate Spot instances and design resumable processing loops that tolerate interruption, keeping infrastructure costs under control as data volume scales</li><li>
Maintain and improve daily job scrapers across multiple sources and build institution data scrapers with robust HTML cleaning pipelines</li></ul>
What we're looking for<ul><li>
1+ years of backend engineering experience focused on data pipelines, ML infrastructure, or search systems</li><li>
Hands-on experience with AWS serverless and container services &mdash; Lambda, ECS Fargate, EventBridge, and Step Functions</li><li>
Strong Python skills &mdash; Pandas, async processing, bulk database operations, and text cleaning</li><li>
Familiarity with vector databases and semantic similarity search; MongoDB Atlas Vector Search experience is a strong plus</li><li>
Cost-conscious infrastructure mindset &mdash; you think in per-record compute costs, free tiers, Spot resilience, and right-sizing</li><li>
Ability to document and communicate complex architecture clearly to both technical and non-technical stakeholders</li></ul>
Nice to have<ul><li>
Experience with knowledge graphs or association rule mining (FP-Growth, Apriori)</li><li>
Experience using LLMs for re-ranking or eligibility assessment on top of vector retrieval results</li><li>
Background in edtech, jobtech, or recommendation/matching systems</li></ul> 

Qualifications 
Degree or existing proven experience 
Additional Information 
Use this instead:
Benefits<ul><li>
Fully remote / work-from-home role</li><li>
Flexible working hours within the team&rsquo;s expected schedule and business needs</li><li>
Opportunity to work on real backend, data, and AI infrastructure projects</li><li>
Exposure to practical engineering challenges in scraping, pipelines, retrieval, and cloud systems</li><li>
Ongoing growth and development within a fast-moving technology environment</li><li>
Opportunity to build long-term value and grow with the company based on performance, including progression and increased responsibility over time</li></ul>
A slightly more polished version:
Benefits<ul><li>
Fully remote / work-from-home position</li><li>
Some flexibility in working hours, depending on team requirements and deliverables</li><li>
Hands-on experience working on meaningful backend, data pipeline, and AI-related systems</li><li>
Opportunity to contribute to a growing platform with real product and engineering challenges</li><li>
Professional growth in a practical, fast-paced environment</li><li>
Strong potential for long-term progression based on performance, regardless of location</li></ul>
If you want it to sound more attractive for hiring, the strongest version would be to add things like:<ul><li>
ownership</li><li>
real product impact</li><li>
career growth</li><li>
direct exposure to architecture and scaling decisions</li></ul></div>

Backend AI & Data Pipeline Engineer

Datamatics Technologies

<div>
 
 Job Title: Data Engineer
 
 
 
 Experience:
 
 3&ndash;5 Years
 
 
 Location:
 
 Remote
 
 
 
 
 Only CV matching with JD will be Shortlisted
 
 
 
 
 Role Purpose
 
 
 We are looking for a skilled
 
 Data Engineer
 
 to design, build, and maintain scalable data pipelines within the Azure ecosystem. You will play a key role in enabling data-driven insights by developing reliable data solutions that support analytics, reporting, and business intelligence initiatives.
 
 
 Key Responsibilities
 
 <ul>
 <li>
 Develop and maintain
 
 ETL/ELT pipelines
 
 using
 
 Azure Data Factory (ADF)
 
 </li>
 <li>
 Work with
 
 Azure Data Lake Storage (ADLS)
 
 for data ingestion, storage, and processing
 </li>
 <li>
 Build and optimize data transformations using
 
 PySpark
 
 and
 
 SQL
 
 </li>
 <li>
 Integrate data from multiple structured and unstructured sources
 </li>
 <li>
 Ensure
 
 data quality, integrity, and consistency
 
 across pipelines
 </li>
 <li>
 Support implementation of
 
 Lakehouse architecture
 
 and
 
 Medallion (Bronze, Silver, Gold) layers
 
 </li>
 <li>
 Collaborate with analysts and BI teams to enable reporting and data models
 </li>
 <li>
 Monitor, troubleshoot, and resolve data pipeline issues
 </li>
 <li>
 Document data processes, workflows, and pipeline logic
 </li>
 </ul>
 
 Required Skills &amp; Experience (3&ndash;5 Years)
 
 <ul>
 <li>
 Hands-on experience with
 
 Azure Data Factory (ADF)
 
 and
 
 ADLS
 
 </li>
 <li>
 Strong programming skills in
 
 PySpark
 
 and
 
 SQL
 
 </li>
 <li>
 Experience in building and maintaining
 
 ETL pipelines
 
 </li>
 <li>
 Working knowledge of
 
 structured and unstructured data processing
 
 </li>
 <li>
 Basic understanding of
 
 Lakehouse architecture
 
 and
 
 Medallion framework
 
 </li>
 <li>
 Familiarity with
 
 Delta Lake concepts
 
 </li>
 <li>
 Strong
 
 problem-solving and debugging
 
 skills
 </li>
 </ul>
 
 Preferred Qualifications
 
 <ul>
 <li>
 Experience with
 
 Azure Databricks
 
 </li>
 <li>
 Understanding of
 
 data modeling and data warehousing concepts
 
 </li>
 <li>
 Exposure to
 
 data governance and data quality frameworks
 
 </li>
 <li>
 Experience working in
 
 Agile/Scrum environments
 
 </li>
 </ul>
 
 Soft Skills
 
 <ul>
 <li>
 Strong communication and stakeholder collaboration skills
 </li>
 <li>
 Analytical mindset with attention to detail
 </li>
 <li>
 Ability to work independently in a
 
 remote setup
 
 </li>
 <li>
 Team player with a proactive approach to problem-solving
 </li>
 </ul>
 
 Career Progression Opportunity
 
 
 This role provides a pathway to grow into a
 
 Senior Data Engineer (5&ndash;10 Years)
 
 position, where responsibilities include:
 
 <ul>
 <li>
 Designing end-to-end data architectures
 </li>
 <li>
 Working extensively with
 
 Microsoft Fabric
 
 and advanced Azure services
 </li>
 <li>
 Leading data engineering initiatives and mentoring team members
 </li>
 <li>
 Optimizing large-scale distributed data systems
 </li>
 </ul>
</div>

Data Engineer

Datamatics Global Services Ltd

<div><h2 class="jobSectionHeader">Job Title: Data Engineer</h2>
Experience: 3&ndash;5 Years 
Location: Remote 
 
Only CV matching with JD will be Shortlisted<h3 class="jobSectionHeader">
Role Purpose</h3>
We are looking for a skilled Data Engineer to design, build, and maintain scalable data pipelines within the Azure ecosystem. You will play a key role in enabling data-driven insights by developing reliable data solutions that support analytics, reporting, and business intelligence initiatives.<h3 class="jobSectionHeader">
Key Responsibilities</h3><ul><li>
Develop and maintain ETL/ELT pipelines using Azure Data Factory (ADF)</li><li>
Work with Azure Data Lake Storage (ADLS) for data ingestion, storage, and processing</li><li>
Build and optimize data transformations using PySpark and SQL</li><li>
Integrate data from multiple structured and unstructured sources</li><li>
Ensure data quality, integrity, and consistency across pipelines</li><li>
Support implementation of Lakehouse architecture and Medallion (Bronze, Silver, Gold) layers</li><li>
Collaborate with analysts and BI teams to enable reporting and data models</li><li>
Monitor, troubleshoot, and resolve data pipeline issues</li><li>
Document data processes, workflows, and pipeline logic</li></ul><h3 class="jobSectionHeader">
Required Skills &amp; Experience (3&ndash;5 Years)</h3><ul><li>
Hands-on experience with Azure Data Factory (ADF) and ADLS</li><li>
Strong programming skills in PySpark and SQL</li><li>
Experience in building and maintaining ETL pipelines</li><li>
Working knowledge of structured and unstructured data processing</li><li>
Basic understanding of Lakehouse architecture and Medallion framework</li><li>
Familiarity with Delta Lake concepts</li><li>
Strong problem-solving and debugging skills</li></ul><h3 class="jobSectionHeader">
Preferred Qualifications</h3><ul><li>
Experience with Azure Databricks</li><li>
Understanding of data modeling and data warehousing concepts</li><li>
Exposure to data governance and data quality frameworks</li><li>
Experience working in Agile/Scrum environments</li></ul><h3 class="jobSectionHeader">
Soft Skills</h3><ul><li>
Strong communication and stakeholder collaboration skills</li><li>
Analytical mindset with attention to detail</li><li>
Ability to work independently in a remote setup</li><li>
Team player with a proactive approach to problem-solving</li></ul><h3 class="jobSectionHeader">
Career Progression Opportunity</h3>
This role provides a pathway to grow into a Senior Data Engineer (5&ndash;10 Years) position, where responsibilities include:<ul><li>
Designing end-to-end data architectures</li><li>
Working extensively with Microsoft Fabric and advanced Azure services</li><li>
Leading data engineering initiatives and mentoring team members</li><li>
Optimizing large-scale distributed data systems</li></ul>
fU4mgrHxuD</div>

Confiz

<div>We are looking for a Data Engineer with 2&ndash;4 years of experience to design, build, and maintain data pipelines that enable efficient data processing and analytics. The ideal candidate will have expertise in SQL, data modeling, and modern data engineering tools and frameworks, and will work closely with data analysts, BI developers, and business teams to ensure reliable and high-quality data flows.
Responsibilities:
<ul><li>Design, develop, and maintain ETL/ELT data pipelines to support business analytics and reporting</li>
<li>Implement data models optimized for performance, scalability, and business needs</li>
<li>Develop and manage data integration workflows using modern ETL tools and orchestration frameworks</li>
<li>Ensure data quality, integrity, and consistency across various systems</li>
<li>Optimize SQL queries and database performance for efficient data retrieval</li>
<li>Collaborate with data scientists, analysts, and BI teams to provide well-structured data for insights</li>
<li>Monitor and troubleshoot data pipeline failures, ensuring minimal downtime</li>
<li>Implement best practices in data engineering, including documentation, version control, and automation</li>
</ul>
Requirements :
<ul><li>2&ndash;4 years of experience in data engineering, ETL/ELT, and data pipeline development (Oracle, Informatica, Azure)</li>
<li>Strong proficiency in SQL for data transformation, query optimization, and performance tuning</li>
<li>Hands-on experience with modern ETL/ELT tools and orchestration frameworks (e.g., ADF, Informatica, Apache Airflow, dbt, or similar)</li>
<li>Knowledge of data modeling principles (dimensional modeling, normalization, denormalization)</li>
<li>Familiarity with relational database systems such as Oracle, MS SQL Server, or similar</li>
<li>Experience with a programming language such as Python for data processing and automation</li>
<li>Strong problem-solving skills with the ability to troubleshoot and optimize data workflows</li>
<li>Ability to work independently and collaborate with cross-functional teams</li>
<li>Good understanding of data governance, security, and compliance best practices</li>
</ul>
Preferred Qualifications :
<ul><li>Bachelor&rsquo;s degree in Computer Science, Engineering, or related field (or equivalent experience)</li>
<li>Experience with cloud data platforms (AWS, Azure, or GCP)</li>
<li>Exposure to CI/CD for data pipelines and version control systems like Git</li>
<li>Familiarity with big data technologies (e.g., Apache Spark) or data warehousing solutions</li>
</ul>
We have a global team of 700 + amazing individuals working on highly innovative enterprise projects &amp; products. Our customer base includes Fortune 100 retail and CPG companies, leading store chains, fast growth fintech, and multiple Silicon Valley startups. 
 
What makes Confiz stand out is our focus on processes and culture. Confiz is ISO 9001:2015 (QMS), ISO 27001:2022 (ISMS), ISO 20000-1:2018 (ITSM) and ISO 14001:2015 (EMS) Certified. We have a vibrant culture of learning via collaboration and making workplace fun. 
 
People who work with us work with cutting-edge technologies while contributing success to the company as well as to themselves. 
To know more about Confiz, visit: https://www.linkedin.com/company/confiz/</div>

Senior Data Engineer

Polarity Holdings

Starting Salary: Rs 501,000 PKR / monthSchedule: U.S. Eastern, Monday through FridayLocation: 100% Remote, Full-TimeAbout the RoleFalcon Scaling and PolarityIQ build private capital markets intelligence products. Our clients are family offices, institutional LPs, fund managers, investment banks, and registered investment advisors. They pay premium prices for verified, decision-maker-level intelligence on capital allocators worldwide.We operate a commercially active library of more than two hundred data products spanning family offices, institutional investors, venture capital, private equity, fund of funds, mutual funds, real estate, and high net worth individuals. Our flagship products include Family Office MAX (25,000+ executives across 10,000+ family offices) and LP MAX Bundle (80,000+ contacts across 17,000+ institutional LPs representing over $2 trillion in AUM).The Senior Data Engineer (Full Chain Builder) owns Project NOVA: the three-stage production pipeline that acquires records at scale, enriches them to the granularity capital allocators will pay for, and activates them inside PolarityIQ through agentic retrieval that surfaces signals autonomously.This is hands-on, high-autonomy work. You will build original scraping pipelines, architect multi-provider enrichment waterfalls, deploy production RAG systems, and build the agentic retrieval layer across our intelligence library. You ship products paying customers rely on, not internal demos.Before You ApplyIf your resume does not name a specific client, a real user volume, and what breaks when your system goes down: do not apply.If you have not deployed at least one production RAG system that a non-technical user relied on: do not apply.If Claude Code is not already part of your daily workflow, this role will move too fast for you.CompensationStarting salary is Rs 501,000 PKR per month. The role includes a revenue-sharing component tied directly to your contributions. You are paid weekly, on time.Strong performers regularly see total earnings scale meaningfully within the first four to eight weeks, with continued upside as contributions compound. On-target monthly earnings can exceed Rs 1,000,000 PKR within six to eight months.How You Enter &mdash; The 30-Day SprintThis role begins with a paid, deliverable-based 30-Day Build and Monetization Sprint at Rs 501,000 per month. You do not need to leave your current position. You operate on your own schedule, deploy existing products against real demand, and report what happens when your work meets the market.This is not a trial period. It is a real operating environment. At day 31, both sides hold 30 days of evidence. You will know whether the earnings trajectory and autonomy are real. We will know how you operate under real conditions.What You'll OwnAcquire &mdash; Sourcing and Scraping<ul><li>Build scraping pipelines against family office sites, regulatory filings, SEC EDGAR, state corporate registration databases, and professional networks.</li><li>Handle production anti-bot environments &mdash; Cloudflare, DataDome, PerimeterX &mdash; through residential proxies, CAPTCHA solvers, and stealth browser configurations.</li><li>Orchestrate recurring scraping with Apache Airflow, Prefect, or equivalent. Scheduled, monitored, recoverable.</li><li>Validate sourced records against schema contracts using Pydantic or equivalent. Every record passes the validation gate before entering the pipeline.</li></ul>Enrich &mdash; Multi-Provider Waterfalls<ul><li>Build and manage multi-source enrichment waterfalls using Clay.com, Apollo, Hunter, People Data Labs, Clearbit, and similar platforms.</li><li>Know the cost, coverage, and reliability tradeoffs between providers field by field. Build waterfalls that minimize cost while maximizing coverage.</li><li>Add granularity dimensions that matter: decision-maker layer, investment thesis, activity signals, relationship graph, structural classification, validation metadata.</li><li>Apply data hygiene principles across all pipelines: bounce rates, email verification, contact scoring, stale record detection.</li><li>Operate orchestration tooling that lets enrichment chains run end-to-end without manual stitching: n8n, Apache Airflow, Prefect, Make, or equivalent. The waterfall is one workflow, not a sequence of disconnected scripts.</li><li>Build cost-efficient pipelines that scale without proportional cost growth.</li></ul>Activate &mdash; Agentic RAG and RetrievalPolarityIQ's core intelligence layer is built on RAG. This is not experimental. It is the foundation of how our platform transforms raw family office data into queryable, actionable intelligence.<ul><li>Architect end-to-end RAG systems: document ingestion, chunking strategy, embedding model selection, vector database architecture, retrieval pipeline design.</li><li>Select and justify vector database choices (ChromaDB, Pinecone, Supabase pgvector, Weaviate, or equivalent) based on cost, latency, and scale requirements.</li><li>Implement hybrid retrieval strategies combining semantic vector search with keyword matching (BM25) for higher relevance and accuracy.</li><li>Build retrieval evaluation loops. Assess whether your system returns accurate, grounded results and diagnose when it does not.</li><li>Design and deploy autonomous AI agents using LangChain, CrewAI, or equivalent frameworks. Agents that gather, validate, and synthesize intelligence across data sources.</li><li>Integrate agentic workflows into the PolarityIQ platform so users receive signal delivery, not just search results.</li></ul>Database Architecture<ul><li>Own the architecture, integrity, and growth of PolarityIQ's core intelligence database.</li><li>Design and manage relational schema across Supabase/PostgreSQL, ensuring normalization, indexing, and query performance at scale.</li></ul>Technical StackScraping and AcquisitionScrapy, Playwright, curl_cffi, BeautifulSoup, Requests. Apify, Phantombuster, Browse AI at production scale. Anti-bot fluency: Cloudflare, DataDome, PerimeterX (named experience, not theoretical). Proxies: Bright Data, Oxylabs, Zyte, Smartproxy. CAPTCHA solvers: 2Captcha, Anti-Captcha, CapSolver. Orchestration: Apache Airflow, Prefect, AWS Lambda.EnrichmentClay.com as primary environment: tables, waterfalls, formulas, AI columns. Provider depth: Apollo, Hunter, People Data Labs, Clearbit, BetterContact, Cognism. Validation: Pydantic, Pandera, or Great Expectations applied to scraping output.RAG and Agentic SystemsProduction RAG deployment covering document ingestion, chunking, embeddings, hybrid retrieval, re-ranking, grounding. Vector databases: ChromaDB, Pinecone, Supabase pgvector, Weaviate. Agent frameworks: LangChain, CrewAI, LlamaIndex. LLM APIs: OpenAI, Anthropic Claude, Gemini, with understanding of cost, latency, and reliability tradeoffs.Claude Code &mdash; Non-NegotiableYou operate Claude Code in terminal-based, agentic workflows. Not as a code completer but as a development collaborator you direct, correct, and build with autonomously. You know its limits and call them without hand-holding.Development and InfrastructureBackend with Python, Node.js, or similar. SQL, relational schema design, query optimization. Managed platforms (Supabase, PostgreSQL). API integration (REST, GraphQL, webhooks). Git / GitHub.Who You AreThe technical skills matter. This section matters more.You take ownership before being asked. You measure yourself by outcomes, not activity. You are honest, even when it is uncomfortable. You are reliable, not occasionally but consistently. You are resourceful enough to treat every dollar spent like it is coming out of your own pocket.Your thought process is disciplined. You verify before you assert. You distinguish between facts, inferences, and conclusions, and you never conflate them. You test your assumptions before acting. You are willing to challenge your own hypotheses when the evidence points elsewhere.You use AI extensively but think independently first. Claude Code feels like a natural extension of how you work. You know when it is wrong, when it is shallow, and when to apply the human layer of judgment no prompt can replicate.You communicate in flawless English. Your first draft should be publishable. You do not require heavy editing cycles.Job Type: Full-timePay: Rs501,000.00 per monthEducation:<ul><li>Master's (Preferred)</li></ul>Language:<ul><li>English (Preferred)</li></ul>Work Location: In person

Research and Development Solutions

<div><div><div><div>Posted date</div> <div>23rd April, 2026</div> <div>Last date to apply</div> <div>23rd May, 2026</div>
</div> 
<div><div>Country</div> <div>Pakistan</div> <div>Locations</div> <div>Islamabad</div>
</div> 
<div><div>Category</div> <div>Data Science
</div></div> 
<div><div>Type</div> <div>Full Time</div> <div>Position</div> <div>1</div>
</div> 
<div><div>Experience</div> <div>3 years</div></div></div></div>
 <div>Terms of Reference (TOR)
Unit: Urban Impact Lab 
Reports to: Team Lead, Urban Impact Lab
About the role

This is a core data role inside the Data Analytics and Systems vertical, Urban Imoact Lab (UIL). The incumbent owns RADS's data infrastructure end to end. That includes designing and maintaining data collection systems, building and running pipelines between survey servers and program databases, managing the database and BI layer behind AapiVerse, developing dashboards across different tools and environments, and automating the recurring extraction, cleaning, and reporting work that keeps field operations and program monitoring functional. The role is hands-on and engineering-first. Research and policy outputs are handled by other verticals; this role exists to make their work possible.

Key responsibilities

Data collection systems (SurveyCTO)
<ul>
<li>Own RADS's SurveyCTO environment: form design, complex skip logic, relevance and constraint rules, encrypted case management, enumerator assignment, review workflows, server administration, and user roles.</li>
<li>Localize and version instruments between English and Urdu, and maintain an internal library of reusable form components.</li>
<li>Build high-frequency checks, back-check workflows, and enumerator performance dashboards that catch data issues within 24 to 48 hours of collection.</li>
</ul>
Data pipelines and ETL
<ul>
<li>Design, build, and maintain production-grade pipelines that move data from SurveyCTO, the MySQL platform, and external administrative sources into a clean, documented, analysis-ready warehouse.</li>
<li>Write clean, version-controlled ETL code in Python (pandas, SQLAlchemy) and Stata; schedule recurring jobs and monitor them.</li>
<li>Maintain raw, intermediate, and output data layers with full lineage documentation, codebooks, and data dictionaries for every dataset produced.</li>
</ul>
Database and BI layer
<ul>
<li>Manage RADS's MySQL database and its schema&rsquo;s, and extend the schema as new modules and studies come online.</li>
<li>Resolve schema issues, relationship conflicts, and performance bottlenecks in coordination with the software team.</li>
</ul>
Dashboard development across tools and environments
<ul>
<li>Build and maintain production dashboards for program management, field operations, donor reporting, and internal analytics, choosing the right tool for the audience and use case.</li>
<li>Work across a mix of BI platforms (Power BI, Tableau, Metabase or Superset) and code-based environments (Python with Streamlit or Dash, R with Shiny, and web-based visualizations with Leaflet or Plotly).</li>
<li>Own the refresh schedule, access controls, and version history for every dashboard in production.</li>
<li>Embed dashboards into the AapiVerse platform and donor-facing microsites where needed.</li>
<li>Retire dashboards that are no longer used and document what replaced them, so the surface area stays manageable.</li>
</ul>
Automation and integration
<ul>
<li>Automate recurring data extraction, cleaning, and reporting tasks.</li>
<li>Build browser-based automation (Playwright) where public or partner data is only accessible through a UI.</li>
<li>Integrate geospatial layers such as catchment polygons, facility locations, and household GPS using GeoPandas and QGIS, and publish web maps where useful.</li>
</ul>
Data governance
<ul>
<li>Maintain IRB compliance, PII handling standards, encryption at rest, access controls, and retention policies across all RADS data assets.</li>
<li>Keep the data inventory, ROPA, and consent artifact records up to date.</li>
</ul>
Team coordination
<ul>
<li>Supervise data officers, junior analysts, and short-term consultants on the vertical's workstreams.</li>
<li>Coordinate with the MeriSehat/AapiVerse software team on platform data structures, UAT cycles, and release planning.</li>
<li>Act as the data point of contact for other RADS verticals including research, program, and partnerships, ensuring their data needs are served reliably.</li>
</ul>Qualifications
Required
<ul><li>Bachelor's or Master's degree in Data Science, Statistics, Computer Science, Economics, Public Health, or a related quantitative field.</li>
<li>3 to 4 years of hands-on experience in data systems, pipelines, and analytics, ideally in a research, health, or development setting.</li>
<li>SurveyCTO: demonstrated experience designing complex forms, managing the server, and running review and back-check workflows.</li>
<li>Python: strong working proficiency in pandas, numpy, and SQLAlchemy; comfort writing unattended scripts and small automations.</li>
<li>Stata: fluency for data cleaning, reshaping, and merging large survey datasets.</li>
<li>SQL and MySQL: able to write non-trivial queries, design schemas, and debug relational models.</li>
<li>Dashboarding: production experience with at least one BI tool (Power BI, Tableau, Metabase, or Superset) and at least one code-based framework (Streamlit, Dash, Shiny, or equivalent).</li>
</ul>Preferred
<ul><li>Experience embedding dashboards into web applications and working with front-end teams on integration.</li>
<li>Experience with geospatial stacks (GeoPandas, QGIS, Leaflet) and browser automation (Playwright, Selenium).</li>
<li>Familiarity with cloud environments (AWS or GCP) and workflow orchestration tools.</li>
<li>Exposure to NIH and FCDO data management and sharing requirements.</li>
<li>Experience building data systems for RCTs or longitudinal household studies.</li>
</ul>Skills and attributes

Engineering discipline, with documentation, reproducibility, and version control as defaults. Comfort moving between a SurveyCTO form, a Python script, a MySQL schema, and a dashboard in the same day. Good judgment about which dashboarding tool fits which audience, rather than defaulting to one. Ability to own multiple concurrent systems without close supervision. Collaborative temperament when working across software, field, and program teams. A preference for durable infrastructure over one-off fixes.

How to apply
<div><ul><li>Curriculum vitae.</li></ul>
<ul><li>A brief cover letter (one page).</li></ul>
<ul><li>A technical artifact of your choice, such as a GitHub repository, a SurveyCTO form export, a dashboard link or file (Power BI, Tableau, Streamlit, or similar), a Stata project, or a data pipeline, that demonstrates your systems work.</li></ul></div></div>

Senior Associate (Data Analytics and Systems)

Job Bank Recruitment Network

DATA ENGINEER &mdash; AI &amp; MODEL TRAININGJob Bank &mdash; AI Business Engine DevelopmentLocation: Remote (Pakistan) Language: Must speak Fluent EnglishSalary: Competitive &mdash; Based on Experience---About Job BankJob Bank is a UK‑focused platform helping people start recruitment and training businesses through two simple annual plans (&pound;100 &amp; &pound;200). We are now building Job Bank AI &mdash; a specialised AI system designed to help users create and scale their own recruitment or training business.Our AI ecosystem includes:<ul><li>Recruitment AI</li><li>Training &amp; Course AI</li><li>Business Builder AI</li><li>Marketing/Outreach AI</li><li>AI Assistants (Recruiter / Trainer / Business Coach / Operations)</li><li>Business Engine (AI Tool Suite)</li></ul>We are building a vertical AI platform, with deep industry‑specific intelligence &mdash; NOT a generic chatbot.This is your opportunity to become a foundational part of a fast‑growing AI product used across the UK.---Role OverviewWe are looking for a Data Engineer who can prepare, structure, clean and optimise datasets for AI fine‑tuning and retrieval systems.Your work will directly power:<ul><li>fine‑tuned recruitment models</li><li>training/course intelligence</li><li>business‑building AI</li><li>outreach and marketing AI</li><li>autonomous AI assistants</li><li>AI-driven games and simulations</li></ul>This role is crucial to the accuracy, reliability and performance of Job Bank AI.---Key ResponsibilitiesData Preparation &amp; Engineering<ul><li>Collect datasets related to recruitment, training, business planning, marketing, and operations</li><li>Clean and standardise raw data (text, CSV, JSON, documents)</li><li>Label and structure data into instruction formats for LLM training</li><li>Build datasets for fine‑tuning small/medium models</li><li>Develop evaluation datasets for model scoring</li></ul>Data Architecture &amp; Pipelines<ul><li>Build and maintain ETL pipelines for AI model ingestion</li><li>Create scalable data workflows to support training cycles</li><li>Manage datasets using tools like Pandas, PySpark or equivalent</li><li>Optimise dataset quality, consistency, and token efficiency</li></ul>Vector Databases &amp; Retrieval<ul><li>Work with embeddings and vector databases (FAISS, Chroma, Pinecone, Weaviate)</li><li>Build and maintain retrieval datasets for RAG systems</li><li>Ensure high‑quality indexing for search, scoring and assistant use</li></ul>AI Model Support<ul><li>Support AI/ML engineers during fine‑tuning cycles</li><li>Prepare domain‑specific datasets for niche models</li><li>Validate and test model outputs against evaluation sets</li><li>Assist with dataset revisions and incremental model improvements</li></ul>Collaboration<ul><li>Work with AI Engineers to support model training</li><li>Work with Full‑Stack Developers to integrate datasets into the platform</li><li>Work directly with the founder to align on roadmap and priorities</li></ul>---Technical Skills (Must Have)Data Engineering<ul><li>Data cleaning, validation, normalisation</li><li>ETL pipeline development</li><li>JSON, CSV, XML processing</li><li>Strong Python (Pandas, NumPy)</li><li>Experience with large text datasets</li><li>Dataset versioning and quality control</li></ul>AI/Data Skills<ul><li>Experience preparing datasets for LLM fine‑tuning</li><li>Understanding of instruction tuning formats</li><li>Embeddings &amp; vector stores (FAISS, Pinecone, Chroma)</li><li>RAG data preparation</li><li>Working knowledge of tokenisation and dataset optimisation</li></ul>General<ul><li>Strong attention to detail</li><li>Ability to work quickly and independently</li><li>Fluent English communication skills</li><li>Experience supporting AI development teams</li><li>Ability to think like a product-focused engineer</li></ul>---Bonus Skills (Nice to Have)<ul><li>Experience with recruitment or training datasets</li><li>Knowledge of course design or HR workflows</li><li>Familiarity with LangChain, LlamaIndex, or similar</li><li>Exposure to machine learning pipelines</li><li>Experience working with international/remote teams</li></ul>---What We Offer<ul><li>Competitive salary (based on expertise)</li><li>Long‑term role with growth potential</li><li>Work on a high‑impact AI product</li><li>A stable, ambitious product roadmap</li><li>Flexible remote working</li><li>Supportive, fast‑paced, visionary team</li><li>Opportunity to build category‑defining AI</li></ul>---Why This Role MattersYou will be the engine behind our AI intelligence. Your datasets will shape:<ul><li>how accurately the AI writes job ads</li><li>how well it scores CVs</li><li>how intelligent the business plans are</li><li>how powerful the training/courses become</li><li>how effective the assistants behave</li></ul>Job Type: Full-timePay: Rs80,000.00 - Rs100,000.00 per monthWork Location: Remote

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