Software Engineering Lead - Data Services
Firmable
IndiaremotePosted 13 days ago
Skill Required
Software-EngineerBackend-EngineerEngineering-LeadSoftware-ArchitectData-EngineerLead-Data-EngineeringData-Engineering-LeadLead-Data-EngineerTech-Lead-Data-EngineeringData-Engineering-Team-LeadTeam-Lead-(Data-Engineering)Data EngineerSoftware EngineerSoftware Developersoftware engineeringEngineeringData WarehousingObservabilityPostgreSQLSnowflakeDynamoDBbuildingFastAPIRESTTestNGPythonAPIsSQLAWSGCPandGenerative AICIAIFulltime
Key highlights
- 7-10 years hands-on backend engineering, including time leading engineers or engineering leads with 3+ years of hands on experience with Python.
- Competitive base + meaningful equity
- Own the engine room — you're not joining a team, you're leading the function
- ~80% hands-on architecture and engineering, ~20% leading the function
Role overview
Firmable is the market-leading B2B sales intelligence platform in Asia Pacific — and we're scaling that success globally at pace. Backed by leading investors and 2,000+ customers, we exist to give sales teams an unfair advantage: the deepest company and people data of any platform, enriched with real-time signals, served at the right moment by intelligent agents. The backend services, APIs, and AI systems behind every Firmable product are the engine room. This role owns it.
Responsibilities
- Backend and API architecture — Firmable's Python services (FastAPI, Pydantic, Logfire): the secure, well-documented backbone every product, internal agent, and external customer depends on
- Data modelling — schemas, access patterns, and integrity at scale across PostgreSQL, Snowflake, and DynamoDB
- AI/LLM services and agents — production systems that are fast, idempotent, observable, and cost-bounded; every LLM call a named, versioned tool with prompts, schemas, traces, and evals
- Harness engineering — the agent runtime the team builds on: tool-calling and MCP server layers, structured-output validation, bounded agentic loops, sandboxed execution, human-in-the-loop checkpoints, replayable traces
- Eval harnesses — scorers, labelled sets, trajectory and multi-turn evals, LLM-as-judge calibration, regression suites in CI, drift detection on vendor model updates
- Scale and reliability — systems serving millions of API calls: idempotency, caching, rate limits, cost ceilings, circuit breakers, bounded retries, graceful degradation
- Observability standard — structured traces, p50/p95/p99 by stage, cost per request, and the dashboards that make it legible
- Function leadership — lead and grow two pods through their leads; run architecture and code reviews; set the bar for quality, testing, and AI-native practice
- Cross-functional glue — connect Data Platform, Application, and Product; turn early ideas into architecture decisions and working prototypes fast
Requirements
- 7-10 years hands-on backend engineering, including time leading engineers or engineering leads with 3+ years of hands on experience with Python.
- Built and operated large-scale production systems — millions of API calls, and you know what breaks first when load grows
- Expert Python with deep production experience in FastAPI
- Shipped AI/LLM services, APIs, and agents in production — and stood up the evals that keep them trustworthy
- Harness engineering experience — you've built the scaffolding agents run inside: tool-calling frameworks, MCP servers, eval and test harnesses other engineers depend on, and you can walk us through it in detail
- Hands-on data modelling across SQL and NoSQL
- Designed and consumed RESTful services at scale
- Shipped real work with agentic IDEs — Claude Code, Cursor, or equivalent. Not "tried it" — your default mode
- Lead from the front — whiteboard to reference implementation to a team operating at that standard
Nice to have
- Eval frameworks (Braintrust, Inspect AI, Promptfoo) and tracing stacks (Logfire, OpenTelemetry)
- Agent frameworks and protocols — Claude Agent SDK, Pydantic AI, LangGraph, MCP
- AWS and/or GCP at scale — ECS/Fargate, Lambda, S3, RDS, DynamoDB
- B2B data, entity resolution, or high-throughput enrichment pipelines
- Snowflake and modern data-platform tooling
- Startup or scaleup experience where you shipped fast and owned outcomes end to end
Benefits
- Competitive base + meaningful equity — a share in the upside we're building toward
Additional details
- As Software Engineering Lead, you'll own the function that designs, builds, and operates the backend services, RESTful APIs, and AI/LLM systems powering every Firmable product. You'll lead two engineering pods through their team leads while staying deeply hands-on yourself. Reporting to the Head of Data & Services.
- This is not a people-management seat. ~80% hands-on architecture and engineering, ~20% leading the function. You whiteboard the system, write the reference implementation, then raise the whole team to that bar. If you want to step away from the code, this isn't it.
- Firmable is an AI-native organisation. Agentic development, AI-driven test generation, automated review pipelines, and built-in evals and traces are the default mode of working, not a productivity experiment. Recurring workflows ship as versioned SKILL.md specs any teammate or agent can run.
- We run lean and ship fast — small senior teams, no layers, minimal process, weekly releases moving toward daily. Teams own their stack end to end. You need to be working this way already, and able to bring a team with you.
- Own the engine room — you're not joining a team, you're leading the function
- Architectural latitude — the next generation of our services and AI platform is yours to shape
- Hands-on leadership — the influence of a lead with the satisfaction of still building
- Massive leverage — your work reaches every Firmable customer, every day
- Firmable is an equal opportunity employer. We believe diverse teams build better products.
- Originally posted on Himalayas