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Role Description

As a Senior Data Engineer, you will be instrumental in designing and building the next generation of our data infrastructure. Your work will handle massive volumes of behavioral, operational, and customer data, directly impacting product features like segmentation, personalization, and marketing effectiveness measurement across our entire MarTech SaaS offering. You will be a key technical leader, responsible for the architecture, reliability, and scalability of our core data assets.

• This role moves beyond data transformation and focuses on architecture, optimization, and technical leadership.
• Architect Scalable Systems: Design, develop, and maintain highly scalable, fault-tolerant, and performant ELT/ETL data pipelines using modern cloud data services to process billions of customer events daily.
• Technical Leadership: Serve as a technical leader within the data team, defining data engineering standards, code review processes, and architectural best practices. Mentor junior and mid-level engineers on complex data challenges and best-in-class solutions.
• Data Modeling & Optimization: Lead the design and implementation of optimized data models (e.g., Data Vault, Kimball, or other dimensional models) in our cloud data warehouse (e.g., Snowflake, BigQuery) to support real-time reporting, internal analytics, and machine learning initiatives.
• Infrastructure as Code (IaC): Implement and manage data infrastructure using Terraform or CloudFormation to ensure reproducible and reliable deployment of data environments.
• Ensure Data Quality & Governance: Establish advanced monitoring, alerting, and automated testing frameworks (e.g., dbt tests, Great Expectations) to enforce high standards of data quality, integrity, and regulatory compliance (GDPR, CCPA) across all data assets.
• Cross-Functional Collaboration: Partner closely with Product Managers, Data Scientists, and Software Engineering teams to translate business requirements into robust, production-ready data solutions that power customer-facing features.

Qualifications

• 5+ years of professional experience in Data Engineering, focused on building large-scale, high-throughput data platforms.
• Proficiency in SQL and extensive experience with modern cloud data warehouses (e.g., Snowflake, Databricks, BigQuery), including advanced concepts like performance tuning, clustering, and materialized views.
• Deep expertise in Python for data engineering, specifically for building and optimizing complex data processing applications.
• Proven experience with a modern data pipeline orchestration tool (e.g., Apache Airflow, Prefect, or Dagster) and data transformation tool (dbt).
• Demonstrable experience with streaming data technologies (e.g., Apache Kafka, Kinesis, or Spark Streaming) to handle real-time customer engagement data.
• Strong understanding of DataOps principles, CI/CD practices, and using Infrastructure as Code (IaC) tools.

Requirements

• Salary range: $108,400 — $135,500 USD.

Benefits

• A generous paid time off policy and a competitive benefits package that supports the health and well-being of you and your family.
• Work flexibility with a hybrid work model combining remote work with access to office locations for collaboration and training.