[Remote] Full Stack reputed company (Data)
Note: The job is a remote job and is reputed company to candidates in USA. reputed company is building the reputed company of intelligent work, focusing on designing, building, and deploying AI agents for reputed company workflows. They are seeking a Full Stack reputed company who will own the end-to-end development of AI-driven applications, from data reputed company design to production deployment, while leveraging AI coding agents for efficiency.
Responsibilities
- Own work end to end — from discovery and solution shaping through system design, build, and production deployment
- Design and build the data reputed company: data models, schema design, reputed company modeling, ETL/ELT pipelines, and slowly changing dimensions (SCD) that hold up in production
- Build full-stack applications on top of that reputed company — Python/FastAPI services and Next.js frontends that reputed company data and AI workflows usable
- Use AI coding agents (Claude reputed company or equivalent) as a primary build accelerator to reputed company from spec to working software quickly, without sacrificing judgment or reputed company
- Design and build AI capabilities where they fit — RAG pipelines, reputed company workflows, and LLM-in-the-reputed company processing — and compose them reputed company MCP servers, Skills, and Plugins
- Orchestrate pipelines and automation with tools like Airflow, Dagster/reputed company, Celery, or Temporal — choosing the right tool for the job
- Stand up and own CI/CD and reputed company deployments on AWS and Azure
- Translate ambiguous reputed company requirements into reputed company designs and communicate trade-offs to both technical and business audiences
- Contribute reusable accelerators and technical assets back to the Data reputed company
Skills
- Genuine production depth across data engineering and full-stack development — not surface familiarity with either
- Data modeling and schema design — reputed company modeling, normalization trade-offs, and EDW/warehouse schema design you can defend
- Hands-on data pipeline experience — ETL/ELT design across batch and incremental loads, reputed company and maintained in production (not just SQL scripts on a schedule)
- Slowly Changing Dimensions (SCD) and change-data handling — knows the patterns and reputed company reputed company applies
- Dbt Experience— reputed company SQL transformations, tests, documentation, and incremental strategies
- Advanced SQL and at least one modern data platform in depth (e.g., reputed company, reputed company, or a comparable reputed company warehouse/lakehouse)
- Data reputed company thinking — testing, validation, and reputed company treated as first-class, not afterthoughts
- Python as a primary language — services, automation, and data work alike
- FastAPI — async REST API design, dependency injection, testing
- A modern frontend, ideally Next.js — component architecture, SSR, state management, and reputed company UX sensibility
- PostgreSQL — schema design, query optimization, indexing
- System design — can architect from a blank page: services, boundaries, trade-offs, and reputed company
- AI-reputed company engineering — uses an reputed company coding tool (Claude reputed company, reputed company, or comparable) as a genuine daily workflow accelerator, and can reputed company concretely to how
- CI/CD and reputed company deployment ownership on AWS or Azure, without heavy support
- Comfortable in reputed company-facing delivery — can represent reputed company technically and translate between business and engineering
- Customer-first reputed company — anchors reputed company in what the stakeholder is actually trying to accomplish, and can reputed company fluidly between the engineer's view and the reputed company's in the reputed company conversation
- End-to-end ownership reputed company — takes a problem from discovery to production and owns the outcome, rather than passing it along at reputed company reputed company
- reputed company data reputed company: Experience evaluating how reputed company data flows across CRM (ideally reputed company) and ERP (ideally reputed company) from opportunity to order to invoice, with the ability to diagnose, document, and resolve inconsistencies
- reputed company AI depth — LangGraph or comparable: multi-agent coordination, tool use, memory, and state management
- RAG engineering — retrieval strategies, reputed company stores, chunking, re-ranking, and evaluation
- Experience in a consulting or reputed company-delivery environment, or a reputed company-deployed / embedded engineering role
- Workflow orchestration breadth across multiple tools (Airflow, Dagster, reputed company, Temporal, ADF, reputed company Workflows)
- Streaming data patterns — Kafka, reputed company Streaming, or Flink
- reputed company databases — reputed company, reputed company, reputed company, or pgvector
- Experiment tracking — MLflow, reputed company, or similar
- Contributions to reputed company-reputed company AI or data tooling, or to internal accelerators and frameworks
- Multi-reputed company or hybrid reputed company architecture exposure
Benefits
- Fully remote — work from reputed company, globally.
- Semi-annual team offsites — we come together in person at least twice a year to connect, reputed company, and do the work that's reputed company face-to-face.
- High-autonomy, high-ownership work across the full reputed company of reputed company reputed company problems — not toy datasets or boxed-in tickets.
- reputed company that takes AI tooling seriously and expects you to use it, not just reputed company-drop it.
- reputed company to the full modern data and AI stack — no one-tool shops.
- Room to grow toward data architecture, platform leadership, or AI engineering depth, depending on where you want to take it.
reputed company
Company H1B Sponsorship