Senior backend, platform, and product engineering across trust, payments, and data systems.
Seattle, WA · 9+ years building backend and platform systems
Abhishek
Bhatnagar
Senior Backend & Platform Engineer | Product Engineering · Data Systems · Reliability
I build backend services, data-intensive products, and platform layers that make complex workflows feel clear, fast, and dependable.
9+ years across Google, BlackRock, and data-heavy product platforms.
Context signals
Built backend platforms and data systems across trust, payments, and financial domains.
Designed APIs, event timelines, ETL workflows, and product surfaces that make cross-system behavior easier to understand.
Comfortable in Python and SQL, with both batch and streaming patterns across modern data stacks.
Strong product partner across engineering, product, data science, and analytics teams in ambiguous environments.
Selected outcomes
Work across platforms, reliability, and data systems
monthly transactions on a financial platform
reduction in manual reporting effort
faster deployment loops and data throughput gains
reconciliation accuracy on automated workflows
Strength areas
Where I’m strongest
Backend Systems and APIs
Service design, domain modeling, and interface choices that keep complex product behavior understandable and dependable as systems grow.
Data Platforms and Pipelines
Real-time ingestion, ETL orchestration, and data quality thinking that stays legible as domains, stakeholders, and reporting needs grow.
Payments and Reliability
Partner-integrated transaction systems, monitoring, and reconciliation-aware design built for durable product behavior rather than fragile happy paths.
Product Engineering
Translate ambiguous user or business pain into product surfaces, system constraints, and delivery plans that technical and non-technical partners can align around.
AI-Assisted Product Workflows
Design systems where retrieval, source fidelity, and output structure matter as much as model quality, especially when the result feeds a real product surface.
Portfolio
Side-projects and case studies
A mix of side-projects and deeper case studies that show product instinct, backend depth, systems thinking, and the tradeoffs behind real software.
Data Products and Public Signals
Historical context
Macro Pulse
A public-data macro platform that ingests rate, inflation, and credit series, computes derived spreads and regime comparisons, and presents them through charts, watch surfaces, and plain-English explainers.
A good macro tool should help users reason about patterns without drifting into investment advice or fake certainty. The useful product work is in freshness, derivation, comparison logic, and narrative discipline.
- FastAPI
- Python
- Pandas
- Plotly
Turns noisy macro series into a full product system with ingestion, normalization, derived metrics, comparison logic, and user-facing summaries that stay descriptive rather than speculative.
B2B Marketplace and Product Platform
Trust before conversion
Indian Specialty Coffee Sourcing Platform
A full-stack B2B sourcing platform for Indian specialty coffee that combines supplier onboarding, lot discovery, readiness-aware ranking, inquiry workflows, and explainable recommendation logic.
In a B2B marketplace, supply discovery alone is not enough. The strongest product turns real-world constraints into structured product inputs so ranking, filtering, inquiry, and follow-up all become more trustworthy.
- FastAPI
- Python
- Postgres
- Supabase Auth
Moves coffee sourcing beyond listing pages by turning logistics, compliance, and supplier readiness into first-class product inputs that improve discovery quality, recommendation trust, and inquiry conversion.
Media Intelligence, Sports Data, and Predictive Product Systems
From feed to system
F1 Intelligence Hub
A production-oriented Formula 1 intelligence platform that combines race-week ingestion, editorial source separation, historical modeling, and a dedicated prediction experience across dashboard and analytics surfaces.
The value is trust plus synthesis. Fans want a faster read on the championship, but they also need to know whether a claim is official, interpretive, or speculative. Once prediction is added, that boundary matters even more. A credible sports intelligence product needs provenance, freshness, explainability, and a path to operational production quality.
- Next.js
- React
- TypeScript
- Tailwind CSS
Turns race-week noise into a source-aware operating layer with live standings, weekend state, editorially separated summaries, and a hybrid ML prediction engine backed by historical race data and explainable feature scoring.
Experience snapshot
Recent roles
Sr. Software Engineer
June 2023 - PresentGoogle (Merchant Product - Trust & Safety, via TelusAI)
Building backend and data products for merchant trust systems where platform reliability, signal quality, and product velocity all matter at once.
Sr. Software Engineer
Jan 2020 - May 2022BlackRock, Inc. (Post Trade & Accounting)
Built cloud-oriented data and reconciliation platforms for post-trade accounting in a high-scale finance environment.
Software Engineer
Aug 2017 - Dec 2019BlackRock, Inc. (Fund Accounting)
Focused on financial data workflows, system performance, and correctness for accounting products with tight operational dependencies.
Writing
Essays and systems notes
Why AI Copilots in Risk Systems Need Evidence, Not Just Answers
In high-stakes review systems, the real product is not a polished answer. It is a defensible decision surface backed by evidence, provenance, and visible uncertainty.
- AI
- Risk systems
- Evidence
What a Social Layer for Spotify Could Actually Look Like
Spotify already has identity, graph structure, listening events, and collaborative objects. The missing piece is not data. It is product shape.
- Consumer products
- Social systems
- Product engineering
Coffee Atlas: Designing a Coffee Product That Teaches Before It Sells
Coffee Atlas started from a simple frustration: most coffee products rush people toward gear and identity before helping them understand what actually changes the cup.
- Coffee
- Consumer products
- Content systems
Contact
Open to senior backend, platform, and product engineering roles
I’m most interested in roles where system design, product judgment, and engineering quality all shape the user experience.
Outside work
What I love doing
Outside work, I spend a lot of time around things that are tactile, slightly obsessive, and hard to do halfway. Music, climbing, coffee, making things, and time outside all scratch the same itch for me: learn the mechanics, get better with repetition, and enjoy the craft while you are at it.
I spend a lot of time around music and play guitar often enough to keep myself honest, and when I am not doing that I am usually bouldering, hiking, paddleboarding, or finding some excuse to be outside. I like building things too, whether that means mechanical tinkering, Lego sets, or overthinking how parts should fit together. I am also very into making and sourcing good coffee, which probably explains the side projects, and very into Formula 1, enough that I built an F1 project instead of limiting myself to complaining about race strategy.
