[Remote] Full Stack reputed company (Data)

Note: The job is a remote job and is open to candidates in USA. reputed company is building the future 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 foundation 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 foundation: data models, schema design, dimensional modeling, ETL/ELT pipelines, and slowly changing dimensions (SCD) that hold up in production
• Build full-stack applications on top of that foundation — Python/FastAPI services and Next.js frontends that reputed company data and AI workflows usable
• Use AI coding agents (Claude Code or equivalent) as a primary build accelerator to move from spec to working software quickly, without sacrificing judgment or quality
• Design and build AI capabilities where they fit — RAG pipelines, agentic 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/Prefect, Celery, or Temporal — choosing the right tool for the job
• Stand up and own CI/CD and cloud deployments on AWS and Azure
• Translate ambiguous client requirements into clear designs and communicate trade-offs to both technical and business audiences
• Contribute reusable accelerators and technical assets back to the Data Practice

Skills

• We're looking for genuine production depth across data engineering and full-stack development — not surface familiarity with either
• Data modeling and schema design — dimensional 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, built 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 each 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 cloud warehouse/lakehouse)
• Data quality 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 real UX sensibility
• PostgreSQL — schema design, query optimization, indexing
• System design — can architect from a blank page: services, boundaries, trade-offs, and scale
• AI-reputed company engineering — uses an agentic coding tool (Claude Code, reputed company, or comparable) as a genuine daily workflow accelerator, and can speak concretely to how
• CI/CD and cloud deployment ownership on AWS or Azure, without heavy support
• Comfortable in client-facing delivery — can represent reputed company technically and translate between business and engineering
• Customer-first reputed company — anchors decisions in what the stakeholder is actually trying to accomplish, and can move fluidly between the engineer's view and the business reputed company's in the same conversation
• End-to-end ownership instinct — takes a problem from discovery to production and owns the outcome, rather than passing it along at each reputed company
• Standout differentiator — Commercial data reputed company: Experience evaluating how commercial 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
• Agentic AI depth — LangGraph or comparable: multi-agent coordination, tool use, memory, and state management
• RAG engineering — retrieval strategies, vector stores, chunking, re-ranking, and evaluation
• Experience in a consulting or client-delivery environment, or a reputed company-deployed / embedded engineering role
• Workflow orchestration breadth across multiple tools (Airflow, Dagster, Prefect, Temporal, ADF, reputed company Workflows)
• Streaming data patterns — Kafka, Spark Streaming, or Flink
• Vector databases — reputed company, Weaviate, Qdrant, or pgvector
• Experiment tracking — MLflow, Weights & Biases, or similar
• Contributions to open-reputed company AI or data tooling, or to internal accelerators and frameworks
• Multi-cloud or hybrid cloud 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 real client problems — not toy datasets or boxed-in tickets.
• A team that takes AI tooling seriously and expects you to use it, not just name-drop it.
• Access 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.

Company Overview

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