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Salary: USD 180,000 - 260,000 / annual
Scowtt is an early-stage startup building AI/ML-powered marketing optimization and autonomous sales experiences. The company integrates CRM, web signals, and product interaction data into real-time systems to help businesses convert leads into customers.
You will own the design, implementation, and production operation of backend and data systems that power Scowtt's marketing AI and sales agents. This is a hands-on, high-ownership role spanning data pipelines, backend applications, analytics systems, and customer data integrations.
Key responsibilities:
- Own backend and data systems end-to-end, from architecture through production operation and scale
- Design and build scalable data pipelines ingesting from CRM, web, advertising, product, and other customer data sources
- Build integrations with data warehouses (BigQuery, Snowflake, Databricks, Redshift) and customer-managed databases
- Design reliable ingestion patterns across APIs, cloud storage, databases, data sharing, batch processing, and event-driven systems
- Build curated datasets and data models used by ML systems, analytics, reporting, and customer-facing applications
- Develop backend APIs and services using Python and TypeScript
- Build systems for large historical backfills and reliable, low-latency incremental processing
- Solve systemic problems around data quality, schema evolution, scalability, observability, reliability, and operational efficiency
- Build tooling for faster, more automated customer onboarding, data validation, configuration, and troubleshooting
- Work across GCP and AWS, making pragmatic architecture decisions
- Contribute to frontend applications when necessary to deliver complete product experiences
- Help define architecture, engineering standards, and reusable patterns as the platform scales
- Mentor other engineers and raise the engineering bar across the team
- Follow and enforce security best practices, including secure coding, sensitive data handling, and authentication/authorization controls
Success means: customer data onboarded and activated significantly faster with less manual work; reliable pipelines as data volume and complexity increase; clean, trusted datasets for ML and product teams; faster team velocity through platforms and tooling you build; scalable technical architecture; proactive problem identification and resolution; durable engineering leverage across the company.
REQUIREMENTS
Must Have:
- Bachelor's degree in Computer Science, Engineering, or equivalent practical experience
- 7+ years of experience building production software systems
- Strong hands-on software engineering experience with Python and TypeScript / Node.js
- Significant experience designing and operating backend services and production data pipelines
- Strong SQL skills and experience working with large datasets and analytical databases
- Experience building and operating systems in GCP and/or AWS
- Demonstrated ability to independently own complex systems from design through production
- Strong problem-solving skills and ability to move quickly in ambiguous environments
Should Have:
- Experience with modern data warehouses (BigQuery, Redshift)
- Experience building ingestion frameworks across APIs, databases, object storage, and event-driven systems
- Strong understanding of distributed systems, data modeling, idempotency, retries, concurrency, and failure recovery
- Experience building curated datasets and data infrastructure supporting ML and analytics workloads
- Experience designing production APIs and backend services at scale
- Experience with cloud services (GCS, S3, Cloud Run, Lambda, SQS/PubSub, Postgres/RDS, or equivalents)
- Strong understanding of monitoring, observability, automated testing, and production operations
- Proven ability to influence technical direction through architecture and execution
Nice to Have:
- Experience integrating with CRM and marketing platforms (Salesforce, HubSpot, Google Ads, Meta)
- Experience building multi-tenant SaaS platforms
- Familiarity with ML training, feature generation, inference, and model-serving workflows
- Experience with modern frontend frameworks (React, Next.js)
- Experience with Terraform or Infrastructure as Code tooling
- Familiarity with ad-tech, mar-tech, sales-tech, or CRM ecosystems
- Experience building developer platforms, data onboarding frameworks, or self-service data tooling
