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 system