Hi, I'm
roleData engineer / PlatformSabbir Hossain
Data engineer. Backend-minded. Platform-first.
I build data and backend systems that need to hold up in the real world. Bell Canada is where I'm doing that now; Johns Hopkins and UofT are the research base I still bring into the work.
Start at the top and read down: UofT, Hopkins, awards, Bell, shipped work, and the kind of role I am aiming for next.
One career graph: what happened, when, and what it proves.
dag career_v9_time_dagschedule @continuousowner @sabbirread_mode time_enhancedrange 2015 - next3 running1 queued
Time-enhanced vertical career pipeline DAG
legend
formationsourceresearchsignalindustryawaiting
mobile DAGtap a layer or swipe the map
real-world classroom
the long way in
Formation Layer
Started Life Sciences, took the long road to figure out the right fit. Odd jobs, course corrections, and the kind of lessons school does not teach - then a clean pivot into the program that worked.
[ 2015 UofT Life Sciences starts ][ 2017 odd jobs + real-world detour ][ 2018 pivot into BioInfo + CompBio specialist ]
specialist degree
specialist base
Source Layer
Bioinformatics & Computational Biology specialist - the right fit. Where the technical base actually got built: CS, bioinformatics, immunology, and systems thinking.
[ 2018 BioInfo + CompBio specialist starts ][ 2022 research roles begin ][ 2024 BSc honours - 3.96 major GPA ]
research runtime
systems before industry
Research Platform Layer
Where research software started becoming production-minded engineering.
[ 2019 UofT research software ][ 2022 Hopkins oncology platform ]
outside proof
outside the code
External Signal Layer
Awards, talks, and manuscripts that made the work visible outside the code.
[ 2023 ABRCMS oral award ][ 2024 Harvard NCRC plenary ][ 2024 ABRCMS poster award ][ 2026 3 manuscripts in review ]
owner + outputs
bell canada · data eng. & AI
Production Runtime
One job. Real ownership of the pipelines, and the live outputs stakeholders actually use.
Ownershippipelines I own end-to-end
Live outputswhat stakeholders consume
[ 2025 Bell Data Eng. & AI ][ now NTS owner + RCA lead ]
platform step
queued
Next Target Layer
The kind of role I want next.
[ next data platform / backend ][ next architecture with ownership ]
I want the first read to answer the obvious questions: what I own now, what I have shipped, and where the receipts are.
The hiring case, without making you dig.
I am comfortable in the messy middle
Pipeline failures, unclear ownership, historical data recovery, and executive-facing RCA work are not side quests for me. That is the kind of work I know how to carry.
NTS owner78,000+ records recovereddirector + VP RCA
I bring the research habits with me
The research background shows up in how I build: provenance, reproducibility, scale, and evidence before hand-waving.
750+ TB multi-omics100+ users3 manuscripts
I like systems that compound
The best next fit is data engineering with backend, infrastructure, and platform ownership: the kind of work that makes teams faster and systems easier to trust.
backend overlapdata contractsoperational design
Data engineering, with platform ownership.
Bell Canada is where I'm doing production work now. The next role should use all of it: data systems, backend judgment, research discipline, and ownership that helps more than one team.
- scale - systems with real users, load, and SLOs
- ownership - messy systems I can make reliable
- platform - work that helps more than one team
- craft - tests, reviews, runbooks, and post-mortems
Every role, project, and proof point on one canvas — click any node, land on the receipts.
Want to trace the work? Open the map.
Best-fit lane: Data engineering / Backend systems / Platform thinking / ETL / ELT pipelines / Data warehousing.
Experience
Open the archiveBell is where I'm focused now. Hopkins and UofT built the depth.
📡 Industry
Data Engineer
Bell Canada
Primary owner of Bell's Network Ticket Service pipeline, with growing ownership across CS Attack RCA work and CSAP delivery for cross-country Bell stakeholders.
78attributes delivered
78,000+records recovered
83%query optimization
- Built and productionized the mission-critical Network Ticket Service data pipeline on Teradata using a three-tier ETL and ELT architecture across staging, warehouse, and analysis layers.
- Integrated four operational systems including REST API event streams, ERP, billing, and directory services using Python and SAS Data Integration while enforcing data contracts and Kimball-style dimensional modeling patterns.
- Built a stateful sessionization algorithm in Python to fix event sequencing defects, refactoring a flawed sequential method into a robust two-pass group-by propagation model.
🧬 Research
Bioinformatics Software Development Research Assistant
Johns Hopkins University
Ongoing spare-time oncology research, full-stack bioinformatics platforms, and ML-driven multi-omics analysis on HPC infrastructure.
750+ TBdata integrated
8novel biomarkers
83%load time reduction
- Reduced analysis load times by 83 percent through optimized caching on a full-stack bioinformatics platform supporting 100+ global researchers.
- Built the platform using Python, R, JavaScript, and C with microservices architecture, SOLID principles, and Docker containerization.
- Engineered scalable ETL pipelines processing over 750 terabytes of multi-omics data on HPC clusters, accelerating biomarker discovery by 40 percent.
🎓 Research
Software Development Research Assistant
University of Toronto
The foundation: software platforms, reproducible research tooling, and workflow automation across multiple wet-lab teams.
30+hours saved weekly
7research teams
50%setup time reduction
- Reduced analysis effort by more than 30 hours per week across 7 research teams by engineering full-stack bioinformatics platforms.
- Built automation using Python, R, C, and Java with object-oriented programming patterns to streamline lab workflows.
- Owned the full software development life cycle from requirements through deployment and maintenance.
Projects
View all projectsSelected systems that show how I build when the work has to ship.
📊Bell Canada
Enterprise Analytics Platform
78-attribute MicroStrategy analytics platform integrating SmartPath, Maximo, IPACT, and LDAP into one decision surface.
Built derived metrics, conditional formatting, cross-filter interactivity, and a structured migration path from development to production with director sign-off.
🔮Bell Canada
NTS/MS Archway Pipeline
Three-tier ETL pipeline integrating SmartPath API, Maximo, IPACT, and LDAP into unified Control Plan reporting.
Processes 150,000+ records in roughly 20 minutes across staging, warehouse, and analytics layers using schema-aware loads across DEV, QA, and PROD.
🔍Bell Canada
Data Quality Recovery System
Full-stack RCA effort that corrected historical data integrity drift and restored analytical confidence.
Executed staged historical recasts correcting 78,000+ records, expanding analytical coverage from 1 month to 9+ months and improving match accuracy to the strongest level since inception.
🧬Johns Hopkins University
Bioinformatics Platform
Open-source full-stack bioinformatics platform used by researchers for visualization, simulation, and analysis workflows.
Built with React, D3.js, R Shiny, Python, WebSockets, and Docker using microservices architecture and SOLID design principles.
🧪Johns Hopkins University
Multi-Omics Data Pipeline
Scalable processing pipeline integrating DISQOVER, ENCODE, PCAWG, PRIDE, and TCGA data for cancer biomarker analysis.
Applied SVM-RFE, Random Forest, and HPC workflows to help identify 8 novel biomarkers and accelerate validation timelines.
🧼Independent build
ProofMark Studio
Hub for the ProofMark document-craft tool line — one catalog of ~50 PDF, text, and publishing utilities built as a single React SPA over a thin FastAPI shell.
Three sibling FastAPI apps (hub, proofmark-pdf, text-cleaner) composed by URL rather than imports, so each surface stays independently editable, deployable, and testable. The hub renders the catalog, routes to each tool, and shares a design system of color tokens and SVG illustrations across tools.
Third-party proof
Outside proof that the work held up in front of real review.
Plenary and oral selections from competitive applicant pools across two national venues.
Verify the talksPoster awards across graduate and national research divisions, beyond the code itself.
Verify the postersThree manuscripts in active review, written from the technical work behind the research.
Open the archiveStack
The tools I actually reach for when the work has to land.
Languages
Core languages for backend, scripting, and analytical work.
Data & ML
ETL pipelines, dimensional modeling, and ML workflows — the data plumbing that has to hold up under real load.
Cloud & DevOps
Deployment, orchestration, infrastructure, and platform operations.
Web & Data Stores
Apps, APIs, and the data systems behind them.
Analytics & Visualization
BI, charting, and exploratory analysis.
Methodology & Delivery
Planning, review, testing, and shipping without chaos.
Foundation
Strong CS and bioinformatics foundations, plus a lot of range.
University of Toronto
- Campus
- St. George Campus
- Degree
- Bachelor of Science (Honours)
- Graduated
- June 2024
- Specialist
- Computer Science, Bioinformatics & Computational Biology
- Minor
- Immunology
- Major GPA
- 3.96 / 4.0
Computer Science
CSC108H1 / CSC148H1 / CSC165H1 / CSC207H1 / CSC209H1 / CSC236H1 / CSC263H1 / CSC373H1
Bioinformatics & Computational Biology
BCH441H1 / BCB410H1 / BCB420H1 / BCB330Y1 / BCB430Y1
Mathematics & Statistics
MAT135H1 / MAT136H1 / STA247H1 / STA237H1
Biochemistry & Immunology
BCH210H1 / BCH311H1 / IMM250H1 / IMM340H1 / IMM350H1
Context
More context, if you want the longer read.
I am a Data Engineer at Bell Canada on the Data Engineering & Artificial Intelligence team. Most of my day-to-day is production pipeline ownership, analytics platform work, cross-domain debugging, and making sure the data layer holds up when people depend on it.
Data Engineer at Bell Canada
Bell Business Markets, Data Engineering & Artificial Intelligence team
5 yrs 9 mo pre-industry
University of Toronto and Johns Hopkins across software, ML, and bioinformatics
Honours BSc, 3.96 major GPA
Computer Science + Bioinformatics specialist with an Immunology minor
Before Bell, I spent 5 years and 9 months building research software across the University of Toronto and Johns Hopkins. I still continue some Hopkins research in my spare time because I genuinely enjoy the work. That path led to Harvard NCRC, oral and poster presentation wins at ABRCMS, 750+ TB of multi-omics data, and three lead-author manuscripts now under review.
The best fit for me right now is primary data engineering work, with a clear path toward data platform engineering and strong overlap with backend or infrastructure-heavy software roles. I like clear abstractions, durable systems, and solving messy technical problems without turning them into a circus.
Data engineeringBackend systemsPlatform thinkingETL / ELT pipelinesData warehousingSQL optimizationDimensional modelingCloud architectureCI / CDTechnical leadership
Still a human being🧠 Learning➗ Mathematics💻 Coding🚀 Space🧬 Bioinformatics🤝 Mentoring🍳 Cooking📚 Reading🎮 Gaming🏃 Fitness
Home base
Canada
Canadian citizen. Fully authorized to work in Canada.
Based in Toronto, Ontario. Home base is clear, with flexibility for the right team setup.
NEXUS card holder. Cross-border travel is easy when the work needs it.
US work status
United States
TN visa eligible. No sponsorship track, lottery, or employer immigration cost burden.
Open to US relocation.
Open to long-term paths. H-1B or green card sponsorship is fine if the role grows that way.
Contact
If you're hiring for serious technical ownership, let's talk.
I'm focused on data engineering, platform, backend, and software roles where architecture, reliability, and follow-through actually matter.