Automation Software Engineer Lead | Private Equity | Ecommerce Full Time

About Us:

Ecomma is a rapidly growing private equity firm with proven expertise in acquiring e-commerce brands worldwide. We excel in a range of areas that drive real, measurable growth, including:

• Value Creation: Strategically investing to maximize the potential of our acquired brands.
• Operational Excellence: Leveraging best practices and a seasoned team to optimize brand operations.
• Sustainable Growth: Prioritizing long-term success through sustainable, smart decision-making.
• Data-Driven Marketing: Using analytics to build marketing campaigns that perform.

Company Overview:

Ecomma is the #1 micro private equity firm acquiring and scaling Shopify ecommerce brands. We’ve acquired 50+ stores and manage $100M+ in revenue.

Ecomma.ai is our AI-powered operating system that consolidates fragmented tools and manual workflows into a unified platform for ecommerce operators. We’re building a multi-tenant SaaS platform that orchestrates complex AI workflows to drive revenue growth and operational efficiency.

You’ll be building the core infrastructure — a distributed, scalable system that handles multi-step AI pipelines, manages state reliably, and serves hundreds of concurrent users across multiple workspaces.

About the Role

Design, build, and operate Ecomma’s automation backbone — connecting every department, centralising all data flows, and enabling the company to scale without its systems becoming the bottleneck.

This is a founding infrastructure role. You will be the first dedicated person to own this problem end-to-end. The CTO sets the architectural direction; you execute it, operate it, and evolve it.

Responsibilities

M&A Research & Deal Intelligence

• Automated research pipelines delivering ranked, enriched targets on a recurring schedule
• AI-powered document intake: unstructured financial and operational records transformed into structured, queryable data assets
• Per-deal knowledge bases with semantic search across all deal-related documents

Sales & CRM Intelligence

• End-to-end communication capture across all inbound channels — auto-linked to CRM records with zero manual logging
• AI-enriched lead records: conversation summaries, sentiment signals, next-step extraction, and priority scoring
• Pipeline hygiene automation: deduplication, stage tracking, and data quality enforcement at the CRM layer

Marketing & Performance Analytics

• Real-time performance data aggregation across paid and organic channels — no manual exports
• Automated anomaly detection and AI-generated alerts across a multi-brand portfolio
• Self-serve analytics dashboards accessible to non-technical stakeholders

Financial Consolidation & Reporting

• Automated ingestion and normalisation of financial data from multiple sources into a unified model
• Budget vs. actuals tracking with real-time variance alerts
• On-demand report generation from live data — no spreadsheet assembly

AI Intelligence Layer

• RAG system over a centralised Knowledge Store: all documents, transcripts, and research semantically queryable by any team member
• AI assistant serving plain-language queries across all company data
• Automated executive digest generation on a scheduled cadence

Core Responsibilities

• Translate CTO architectural direction into production-grade automation systems across every department
• Design, build, and operate multi-source data pipelines with monitoring, alerting, and self-healing retry logic
• Own all CRM integrations as the single source of operational truth
• Build and maintain the data warehouse layer powering company-wide dashboards and analytics
• Design and deploy the RAG-based AI assistant serving plain-language queries against all company data
• Build lightweight internal web tools and dashboards for department use
• Enforce data quality standards: validation, deduplication, schema versioning, and incident response
• Document every system built — architecture diagrams, data flow maps, runbooks, and operational guides
• Communicate system outputs clearly to non-technical stakeholders across all departments

Must Have:

Backend Engineering

• Python — primary language for data pipelines, document processing, AI/ML workflows, and scraping services
• Node.js / TypeScript — service layer, webhook handlers, CRM integrations, and browser extension backend
• REST API design, webhook ingestion, and event-driven architecture
• Async job processing with reliable retry logic, idempotency guarantees, and dead-letter handling
• Structured logging and observability sufficient to debug production pipelines

Data Engineering & Analytics

• Airbyte — ELT connector layer; native connectors for Shopify, Meta Ads, Google Ads, TikTok Ads, and HubSpot feeding the warehouse
• dbt — SQL-based transformation layer; data modelling, testing, incremental materialisation, and source documentation
• BigQuery / Redshift / Snowflake — cloud data warehouse; deep knowledge of one required; schema design, cost-aware querying, partitioning, and materialised views
• PostgreSQL — operational database; schema design, migrations, indexing, and query optimisation
• Advanced SQL — window functions, CTEs, aggregations, cross-source joins, and query plan analysis at warehouse scale

Analytics & BI

• Metabase / Apache Superset — self-serve analytics dashboards; knowledge of one required; connecting warehouse data models to visual dashboards accessible to non-technical stakeholders; scheduled reports and embedded analytics

RAG & Vector Search

• pgvector / Qdrant — vector storage for the document knowledge store; embedding pipeline design, cosine similarity and hybrid (dense + sparse) search, retrieval quality evaluation; this is a core deliverable — the semantic queryability of all company documents, transcripts, and research depends on it

Document Processing

• pdfplumber / PyMuPDF — financial document parsing; extracting structured data from PDFs, bank statements, invoices, and due diligence records
• LlamaIndex — document ingestion pipeline design; chunking strategies, metadata filtering, and retrieval evaluation on top of the vector store

CRM Engineering

• HubSpot API / Salesforce API — deep CRM integration experience with one of the two; contacts, deals, pipelines, custom objects, webhook triggers, property automation, and rate-limit-aware write patterns; every other system in this role feeds into the CRM

Workflow Orchestration

• n8n — automation flow engine connecting the CRM, APIs, form submissions, webhook triggers, and scheduled jobs across departments
• Temporal — code-first long-running workflow orchestration; DAG design, scheduling, failure recovery, and idempotency for complex multi-step pipelines

LLM Integration & Agentic AI

• OpenAI API / Anthropic Claude / Google Gemini — production LLM integration; structured output, function calling, context window management, and cost-aware model routing; experience with open-source models (Llama 3, Mistral) a meaningful differentiator
• LangChain — document processing chains, retrieval pipelines, document loaders, and prompt templating
• LangGraph — stateful multi-agent workflows; cyclic reasoning graphs, persistent memory, tool registries, and human-in-the-loop approval nodes
• LangFlow — visual LangChain pipeline builder; flow design, custom component authoring, and REST API deployment of agentic pipelines

Web Automation & Scraping

• Playwright — headless browser automation; DOM interaction, network interception, scheduled scraping, and anti-bot evasion patterns

Browser Extension Development

• Chrome Manifest V3 — content scripts, background service workers, DOM data capture, and CRM write-back from extension context

Frontend Engineering

• React + TypeScript — component-driven dashboards, admin interfaces, real-time data views, and role-based access; this is a technical leadership role — the person owns the full stack including internal tooling UI
• Next.js — full-stack internal tools with API routes and server-side rendering

Infrastructure & Development

• GCP / AWS — managed services; deep knowledge of one required; service deployment (Cloud Run or Lambda), object storage, async messaging (Pub/Sub or SQS), and IAM
• Docker — containerising all services, scrapers, and pipeline workers
• GitHub Actions — CI/CD; automated build and deploy pipelines with rollback

Professional Baseline

• 5+ years of software engineering with a demonstrable automation or data engineering track record
• Technical leadership capability — architectural decision-making, system documentation, and clear communication of technical trade-offs to non-technical stakeholders
• Comfortable driving multiple independent workstreams from design through production with minimal supervision

What Success Looks Like

A Senior Software Engineer with a demonstrated automation and data engineering track record — enough technical breadth to move across backend, data, AI, and analytics domains, and enough problem-solving depth to navigate gaps independently.

You will not be handed a ticket queue. You will be given architectural direction from the CTO and trusted to translate it into production systems the whole company depends on.

You build for longevity, not for speed. You document what you build. You ask architectural questions before writing code. You understand that an automation system that breaks in six months is worse than no automation at all.

You are comfortable working across every department — gathering requirements, running demos, explaining technical outputs to non-technical colleagues — while staying anchored to the technical standards the CTO sets.

How To Apply

https://ecomma-ai-job-application.web.app/?job=automation-lead

We are looking for senior engineers only. If you are early-career or primarily a specialist in one layer of the stack, this is not the right role.