Senior Data Platform Engineer (Python, Airflow)

We are looking for a Senior Lifecycle Platform Engineer (Pipeline-First) to join an embedded, outcome-focused engineering team.

This is a hands-on individual contributor role focused on designing and operating production-grade data pipelines that power lifecycle messaging systems at scale.

The core mission: replace fragile, manual campaign workflows with reliable, observable, automated pipeline systems, and harden the infrastructure that powers email and SMS delivery for tens of millions of users.

What You Will Work On

• Design and build production-grade Python pipelines (Airflow DAGs) that automate lifecycle messaging workflows end-to-end.
• Replace manual processes (audience prep, segmentation, suppression, send execution) with reliable, scheduled, testable systems.
• Engineer SQL-driven audience selection logic at scale (BigQuery or equivalent) Segmentation, suppression lists, consent/DNC compliance, and idempotency patterns to prevent double-sends.
• Build observability across messaging pipelines (Datadog or equivalent) Instrument pipelines with alerts, dashboards, and monitoring to catch failures before they impact spend or delivery
• Integrate machine learning model outputs into pipeline workflows
  Use model scores to control send volume, targeting, and campaign decisioning
• Work with Iterable (or equivalent ESP) at the API level
  Configure campaigns, triggers, and data flows programmatically
  ESP onboarding support is provided — prior ESP experience is a plus, not required
• Configure backend data for dynamic email templates
  Wire property data (name, ID, images, attributes) into templates to enable personalization and A/B testing
• Standardize messaging logic across channels (email, SMS, push)
  Unify trigger thresholds and decisioning across systems

What We Are Looking For

Core Requirements 

• Strong Python in a data pipeline context
  Airflow DAGs, pipeline scripting, automation, data integrations
  Not web frameworks (Django/FastAPI/Flask)
• Production pipeline engineering experience
  Scheduling, dependency management, failure handling, retries, idempotency, monitoring
• Airflow (or equivalent orchestration tools)
  Prefect, Luigi, or similar tools are acceptable
• Strong SQL + data warehouse experience
  BigQuery preferred; Snowflake/Redshift acceptable
  Experience with large-scale audience segmentation and data transformations.
• Observability mindset - Experience with Datadog or equivalent monitoring tools.
• 6+ years of backend or platform engineering in production environments.
• Fluent English.

Nice to Have 

• ESP API experience (Iterable, Braze, Klaviyo, Salesforce Marketing Cloud, etc.)
• Messaging infrastructure experience (email/SMS/push systems)
• Experience integrating ML model outputs into production systems
• Background in B2C or marketplace platforms

What This Role Is NOT

• Not a Python web development role
• Not a pure data engineering role (ETL-only profiles are not a fit)
• Not a marketing automation specialist role

Why You Should Apply

• Remote — work from anywhere in Latin America
• USD compensation
• Paid time off
• High-impact role — own systems that power messaging at massive scale
• Work on real production systems from day one
• Strong learning and growth opportunities in pipeline engineering and platform systems