Requirements

Python & Big Data Expertise: Advanced proficiency in Python and proven experience architecting, deploying, and maintaining Big Data and streaming/batch pipelines (e.g., Kafka Streams, Event Streaming, Trino, Iceberg).
dbt & Airflow Proficiency: Hands-on experience using dbt for scalable data transformations and Airflow for workflow orchestration.
Data Architecture Mastery: A strong background in designing scalable, fault-tolerant data architectures, implementing data quality frameworks, and establishing production best practices.
Product & Discovery Mindset: Ability to take a vague problem statement, independently uncover the requirements, scope project milestones, and drive solutions end-to-end.
Cross-functional Stakeholder Management: Excellent communication skills with the ability to bridge tech, marketing, and analytics, turning marketing needs into crisp engineering roadmaps.

Nice to Have

Experience with MarTech tooling or reverse ETL setups (e.g., HighTouch, Braze, vendor API integrations).
Familiarity with modern AI/ML data integration practices.
Background in fintech or fast-scaling tech platform environments.

Drive Data Architecture & Resilience: Audit, map out, and elevate our current data pipelines and streaming architectures. Establish best practices for monitoring, reliability, and scale across our data ecosystem (using Python, dbt, Airflow, Kafka, and Trino/Iceberg).
Build the Unified Customer View: Productionize PoCs into scalable Airflow/dbt data workflows to lay the groundwork for our Customer Data Platform (CDP) datasets.
Bridge Engineering & Analytics: Partner with analytics leads to define clear boundaries and standards for data ingestion, cleansing, and transformation—bringing a strong analytics engineering mindset (specifically via dbt) to raw data landing.
Own MarTech Infrastructure: Work alongside Engineering Leads to bring technology ownership of MarTech systems (e.g., Braze, HighTouch reverse ETL) in-house, building end-to-end solutions rather than isolated data plumbing.
Technical Scoping & Discovery: Take ownership of broad, ambiguous problem spaces. Uncover hidden challenges, propose robust architectural designs, and execute your own roadmap.
Coach & Mentor: Elevate the data capabilities of software engineers in the squad through code reviews, architectural guidance, and hands-on mentoring.