Job description

About Scowtt Scowtt is an early-stage startup transforming the way businesses convert leads into customers through AI/ML marketing optimization and fully autonomous sales experiences. By integrating CRM, web signals, and product interaction data into real-time systems, we help businesses turn interest into action—immediately. We’re growing fast, and building scalable data infrastructure and applications that allow us to onboard, understand, and activate customer data quickly is core to our success. About the Role We are looking for a Senior / Staff Software Engineer — Data Platform to build and evolve the data and application systems that power our Marketing AI and AI Sales Agents. This is a hands-on engineering role with significant ownership across data pipelines, backend applications, analytics systems, and customer data integrations. You will build systems that ingest data from a wide variety of customer platforms, transform it into reliable and curated datasets, and make that data available to our ML systems, analytics applications, and customer-facing products. The role is primarily backend focused, but we are a startup—you should be comfortable working across the stack and occasionally building frontend functionality when needed to get a product or capability shipped. We value engineers who can take an ambiguous problem, determine the right technical approach, and drive it independently from design through production. What You’ll Do Own backend and data systems end-to-end, from architecture and implementation to production operation and scale Design and build scalable data pipelines for CRM, web, advertising, product, and other customer data Build integrations with data warehouses and platforms such as 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, services, and internal applications using Python and TypeScript Build systems for large historical backfills as well as reliable, low-latency incremental processing Solve systemic problems around data quality, schema evolution, scalability, observability, reliability, and operational efficiency Build tooling that makes customer onboarding, data validation, configuration, and troubleshooting faster and more automated Work across GCP and AWS and make pragmatic architecture decisions based on the problem at hand Occasionally 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, proper handling of sensitive data, authentication/authorization controls, and compliance with company security policies Qualifications 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 and platforms such as BigQuery, or 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 machine learning and analytics workloads Experience designing production APIs and backend services at scale Experience with cloud services such as GCS, S3, Cloud Run, Lambda, SQS/PubSub, Postgres/RDS, or equivalent technologies 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 such as Salesforce, HubSpot, Google Ads, Meta, or similar systems Experience building multi-tenant SaaS platforms Familiarity with ML training, feature generation, inference, and model-serving workflows Experience with modern frontend frameworks such as React or Next.js Experience with Terraform or other 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 What Success Looks Like Customer data can be onboarded and activated significantly faster with less manual engineering work Data pipelines remain reliable as data volume, customer count, and integration complexity increase ML and product teams have clean, trusted, and reusable datasets they can build on Teams move faster because of the platforms, abstractions, and tooling you build Technical architecture scales cleanly with product ambition You identify problems proactively and drive them to resolution without requiring detailed direction You create durable engineering leverage across the company Pay range and compensation package Based on the level hired at and location, the base salary for this role will be in the range $180,000 - $260,000. Selected candidate/s will be eligible for standard benefits and attractive startup equity. Equal Opportunity & E-Verify Employer Scowtt is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, or veteran status. We are committed to providing a workplace free of discrimination and harassment. We participates in E-Verify and will provide the federal government with your Form I-9 information to confirm that you are authorized to work in the U.S.

Requirements

Must-haves

What You’ll Do
Own backend and data systems end-to-end, from architecture and implementation to production operation and scale
Design and build scalable data pipelines for CRM, web, advertising, product, and other customer data
Build integrations with data warehouses and platforms such as 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, services, and internal applications using Python and TypeScript
Build systems for large historical backfills as well as reliable, low-latency incremental processing
Solve systemic problems around data quality, schema evolution, scalability, observability, reliability, and operational efficiency
Build tooling that makes customer onboarding, data validation, configuration, and troubleshooting faster and more automated
Work across GCP and AWS and make pragmatic architecture decisions based on the problem at hand
Occasionally contribute to frontend applications when necessary to deliver complete product experiences

Nice-to-haves

Experience integrating with CRM and marketing platforms such as Salesforce, HubSpot, Google Ads, Meta, or similar systems
Experience building multi-tenant SaaS platforms
Familiarity with ML training, feature generation, inference, and model-serving workflows
Experience with modern frontend frameworks such as React or Next.js
Experience with Terraform or other 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
What Success Looks Like
Customer data can be onboarded and activated significantly faster with less manual engineering work
Data pipelines remain reliable as data volume, customer count, and integration complexity increase
ML and product teams have clean, trusted, and reusable datasets they can build on
Teams move faster because of the platforms, abstractions, and tooling you build

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimlpythontypescriptgcpawsnode.jssqlmachine learningpostgresreactnext.js

Platform: greenhouse
Effort: quick