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 Originally posted on Himalayas