Forward Deployed Engineer

Overview

Build and own production-grade data foundations and full-stack AI applications for client delivery, working end to end from discovery through deployment.

What you'll do

• 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, using Python/FastAPI services and Next.js frontends that make 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, including RAG pipelines, agentic workflows, and LLM-in-the-loop processing, and compose them via 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.

What you'll need

• Data modeling and schema design, including dimensional modeling, normalization trade-offs, and EDW/warehouse schema design you can defend.
• Hands-on data pipeline experience with ETL/ELT design across batch and incremental loads, built and maintained in production.
• Knowledge of Slowly Changing Dimensions (SCD) and change-data handling patterns, and when each applies.
• dbt experience, including modular SQL transformations, tests, documentation, and incremental strategies.
• Advanced SQL and at least one modern data platform in depth (e.g., Snowflake, Databricks, or a comparable cloud warehouse/lakehouse).
• Data quality thinking, with testing, validation, and lineage treated as first-class concerns.
• Python as a primary language for services, automation, and data work alike.
• FastAPI experience, including async REST API design, dependency injection, and testing.
• A modern frontend, ideally Next.js, including component architecture, SSR, state management, and real UX sensibility.
• PostgreSQL experience, including schema design, query optimization, and indexing.
• System design ability to architect from a blank page: services, boundaries, trade-offs, and scale.
• AI-paired engineering using an agentic coding tool (Claude Code, Cursor, or comparable) as a genuine daily workflow accelerator, with concrete ways of applying it.
• CI/CD and cloud deployment ownership on AWS or Azure, without heavy support.
• Comfort in client-facing delivery, able to represent the company technically and translate between business and engineering.
• A customer-first mindset that anchors decisions in what the stakeholder is trying to accomplish and moves fluidly between engineering and business owners.
• An end-to-end ownership instinct, taking a problem from discovery to production rather than passing it along at each handoff.

Nice to have

• Commercial data fluency: experience evaluating how commercial data flows across CRM (ideally Salesforce) and ERP (ideally NetSuite) from opportunity to order to invoice, with the ability to diagnose, document, and resolve inconsistencies.
• Agentic AI depth with LangGraph or comparable, including multi-agent coordination, tool use, memory, and state management.
• RAG engineering, including retrieval strategies, vector stores, chunking, re-ranking, and evaluation.
• Experience in a consulting or client-delivery environment, or a forward-deployed / embedded engineering role.
• Workflow orchestration breadth across multiple tools (Airflow, Dagster, Prefect, Temporal, ADF, Databricks Workflows).
• Streaming data patterns using Kafka, Spark Streaming, or Flink.
• Vector databases such as Pinecone, Weaviate, Qdrant, or pgvector.
• Experiment tracking with MLflow, Weights & Biases, or similar.
• Contributions to open-source AI or data tooling, or to internal accelerators and frameworks.
• Multi-cloud or hybrid cloud architecture exposure.

Details

• Location: India.
• Semi-annual in-person team offsites, at least twice a year.