Senior Data Engineer - Scalable Growth

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.

What You'll Be Doing

• 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.

Perks and Benefits