Senior Software Engineer- Enrichment

Senior Software Engineer – Enrichment (RevSpring)

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📌 Role Overview

The Senior Software Engineer on the Enrichment team builds and maintains AI‑driven data‑enrichment pipelines that augment RevSpring’s core data platform. You’ll work closely with other engineers to design scalable, secure, and high‑performance solutions that ingest third‑party data, apply AI/LLM‑based transformations, and expose the enriched data to downstream services.

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🛠️ Core Responsibilities

Area	What You’ll Do
AI‑Powered Enrichment	Design, implement, and evolve provider‑data enrichment engines using Python, LangChain/LangGraph, and LLMs.
Integration Architecture	Build robust connectors to 3rd‑party data sources (APIs, bulk feeds, streaming) and sync them with internal BigQuery / relational stores.
AI‑Assisted Development	Leverage AI‑driven coding assistants, automated test generation, and static analysis to keep code quality high.
Code Review & Mentorship	Review peer PRs, give constructive feedback, and champion maintainable, well‑documented code.
Requirements & Documentation	Participate in requirement grooming, write technical specs, create architecture diagrams, and maintain up‑to‑date docs.
Performance & Reliability	Anticipate scaling bottlenecks, instrument services for observability, and enforce security best practices.

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🧩 Technical Stack

Category	Technologies
Backend	Python (OOP), LangChain / LangGraph, Docker, Pulumi (IaC)
Data	BigQuery, relational (PostgreSQL/MySQL) & NoSQL (e.g., DynamoDB, Firestore)
Cloud	GCP or AWS (experience with both is a plus)
Frontend	TypeScript, React (for internal tooling / dashboards)
Version Control / CI	Git, GitHub/GitLab, Docker‑based pipelines
Observability	Cloud‑native monitoring (Stackdriver, CloudWatch), logging, tracing

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🎯 Minimum Qualifications

Skill	Details
Programming	5+ years Python (strong OOP), 5+ years building production services.
Databases	5+ years with relational & NoSQL databases; comfortable writing performant queries and schema design.
Frontend	Proven experience with TypeScript & React (building internal UIs, dashboards, or admin tools).
Cloud & Containers	Hands‑on Docker; experience deploying to GCP or AWS; familiarity with IaC (Pulumi).
Data Engineering	Experience cleaning, normalizing, and enriching messy, large‑scale datasets.
Performance & Security	Ability to anticipate performance impacts, design for scalability, and apply networking & security best practices.
Communication	Translate complex technical trade‑offs into clear language for non‑technical stakeholders; write maintainable documentation.
Collaboration	5+ years using Git in team environments; comfortable with code reviews and pair programming.
Education	Bachelor’s degree (any discipline).

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🤝 Soft Skills & Traits

• Problem‑Solver: You enjoy tackling “dirty data” challenges and turning chaos into actionable insight.
• Architectural Thinker: You design for the future—considering latency, cost, and security before they become issues.
• Mentor: You give and receive feedback constructively, raising the overall quality of the team’s output.
• Curious Learner: You stay current with AI/LLM tooling (LangChain, LangGraph) and evaluate new libraries for fit.
• Stakeholder‑Focused: You can explain why a particular data‑modeling decision matters to product, compliance, or business teams.

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📄 Suggested Resume Highlights (for candidates)

Section	Example Bullet Points
Professional Summary	“Senior Software Engineer with 7 years of experience building AI‑enhanced data pipelines on GCP/AWS, specializing in Python, LangChain, and large‑scale data enrichment.”
Technical Experience	• Designed a LangChain‑based provider‑enrichment engine that increased data coverage by 35 % while reducing manual curation time by 60 %. • Built a Pulumi‑driven CI/CD pipeline deploying Dockerized micro‑services to GCP Cloud Run, achieving zero‑downtime releases. • Integrated 12+ third‑party APIs (REST, S3, Pub/Sub) into a unified BigQuery data lake, handling >10 TB/month of raw data.
Frontend Projects	• Developed an internal React dashboard (TypeScript, Redux) for monitoring enrichment job health, reducing incident response time by 40 %. • Implemented role‑based access controls and secure token handling following OWASP guidelines.
Performance & Security	• Optimized SQL queries and introduced sharding, cutting query latency from 2 s to 300 ms. • Conducted threat modeling for data ingestion pipelines; applied VPC Service Controls and IAM least‑privilege policies.
Collaboration & Leadership	• Led weekly architecture review sessions; mentored 4 junior engineers on clean‑code practices and test‑driven development. • Authored comprehensive design docs and data‑flow diagrams for cross‑functional teams.

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🗂️ Interview Preparation Tips

Area	What to Expect / How to Prepare
System Design	Be ready to design an end‑to‑end enrichment pipeline: data ingestion, transformation (LLM/AI), storage, and API exposure. Discuss scaling, fault tolerance, and cost considerations on GCP/AWS.
Coding (Python)	Expect OOP‑focused problems, data‑manipulation tasks, and possibly a LangChain‑related mini‑project. Write clean, testable code with type hints.
SQL/NoSQL	Write performant queries, discuss schema design for semi‑structured data, and explain when you’d choose a relational vs. NoSQL store.
Frontend	You may be asked to build a small React component (e.g., a status table) or discuss state management and TypeScript typing strategies.
Cloud & Infra	Explain Docker containerization, Pulumi/IaC concepts, and differences between GCP services (BigQuery, Cloud Run, Pub/Sub) vs. AWS equivalents.
AI/LLM	Demonstrate familiarity with LangChain/LangGraph, prompt engineering, and how you’d evaluate model outputs for data enrichment quality.
Behavioral	Prepare stories around handling messy data, influencing non‑technical stakeholders, and improving code maintainability. Use the STAR method.

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✅ Quick “Fit” Checklist

✔️	Requirement	Do you meet it?
1	5+ years Python (OOP)
2	5+ years relational & NoSQL DBs
3	5+ years Git collaboration
4	TypeScript/React experience
5	Docker + GCP/AWS (or both)
6	Experience with messy, large‑scale data
7	Ability to explain technical trade‑offs to non‑technical audiences
8	Bachelor’s degree (or equivalent experience)

If you tick most of the boxes, you’re a strong candidate for this role.

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📚 Resources to Brush Up

• LangChain/LangGraph docs – focus on data‑loader, retriever, and graph‑based workflow examples.
• Pulumi + GCP/AWS – quick‑start tutorials for deploying a Docker container to Cloud Run / ECS.
• BigQuery performance tips – partitioning, clustering, and materialized views.
• React + TypeScript patterns – “compound components”, “render props”, and strict typing for API responses.
• Data‑cleaning best practices – “The Data Wrangler’s Handbook” (free PDF) for handling missing/duplicate records at scale.

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Bottom line: This role sits at the intersection of AI‑driven data engineering, cloud infrastructure, and full‑stack development. Demonstrating depth in Python/LLM pipelines, solid cloud/container expertise, and the ability to ship maintainable front‑end tools will set you apart. Good luck!