Lead Data Engineer

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🕒 May 30

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Vista

WebsiteLinkedInAll Job Openings

5001 - 10000 employees

Founded 1995

🤝 B2B

🛍️ eCommerce

💰 $40M Venture Round on 2003-07

B2B • eCommerce • Design/UX

Vista is a remote-first company dedicated to empowering small businesses by providing exceptional services in technology, design, marketing, and customer care. With a global workforce of over 6,500 team members across 17 countries, Vista emphasizes collaboration, inclusivity, and sustainability. Their commitment to innovation enables small businesses to thrive, while the company's culture fosters personal and professional growth for its employees.

📋 Description

• Architect & Lead Operational Data Flows by designing and overseeing the implementation of an Operational Data Store (ODS) • Build low-latency data streams using technologies like Kafka or Flink to power embedded analytics directly within customer-facing applications • Establish "Data Contracts" with upstream engineering teams to ensure high availability and schema stability for all real-time operational flows • Own the transition and scaling of our Analytical Data Store (e.g., Snowflake), ensuring it is optimized for both performance and cost-efficiency • Modernize transformation layer by implementing robust ELT patterns and modular data modeling (using dbt and airflow) • Champion Data Governance, ensuring that every dashboard and report is backed by high-quality, audited, and well-documented data • Build the "Data Foundation" for Machine Learning, including development of Feature Stores and automated pipelines for model training and inference • Mentor and grow a high-performing engineering team, fostering a culture of "DataOps" where automation, testing, and observability are the default • Act as a strategic partner to Product and Executive leadership, translating complex technical roadmaps into clear business value

🎯 Requirements

• 8+ years in Data Engineering, with at least 3+ years in a formal leadership or management role • Proven experience architecting cloud data warehouses (Snowflake, BigQuery, or Databricks) • Expert-level proficiency in Python (for automation/pipelines) and SQL (for complex modeling and optimization) • Proficiency in AWS infrastructure management and event-driven pipelines (Kinesis, IAM, Monitoring, and IaC frameworks) • Hands-on experience with stream processing tools (Kafka, Flink, or Spark Streaming) • Ability to design ELT/ETL architectures from scratch using dbt, with a focus on idempotency, scalability, and error handling. • Experience implementing data quality frameworks (e.g., Great Expectations, Monte Carlo) and ensuring compliance (GDPR/CCPA) • Experience in a "Product-led" organization where engineering is a value-driver • Ability to communicate complex architectural constraints (like latency or data consistency) to non-technical partners in terms of business impact and ROI • Proven track record of working with Product Managers to ship data-intensive features in an Agile environment

🏖️ Benefits

• Remote-First operating model and culture • Collaboration spaces for team members to work physically together