Director, Data Engineering

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November 3

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Revinate

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Enterprise • Hospitality

Revinate is a company specializing in the hospitality industry, providing products and solutions aimed at enhancing guest experiences and increasing direct revenue for hotels. The company's offerings include Revinate Guests, a dashboard for managing guest profiles, and Revinate Marketing, an email marketing solution designed to deliver higher revenue through smart, segmented campaigns. Additional services include a text messaging solution, a highly accurate reservation contact center, and reputation management tools for increasing review volumes. Revinate serves over 12,000 hotels globally, helping them generate substantial direct revenue and gain a competitive edge in the hospitality sector.

501 - 1000 employees

Founded 2012

🏢 Enterprise

💰 $39.2M Series E on 2021-08

📋 Description

• Define and execute the long-term vision and roadmap for our data platform, aligning with overall business objectives and technology strategy. • Recruit, mentor, and lead a talented team of data engineers, fostering their growth and ensuring best practices in data engineering. • Oversee the design, development, and maintenance of scalable and reliable real time data ingestion pipeline, ensuring data quality, accuracy, and timely delivery. • Lead the architecture and management of our operational data stores, optimizing for performance, reliability, and accessibility to support critical business applications. • Drive the strategic evolution and implementation of our data lakehouse, enabling unified data access, advanced analytics, and machine learning initiatives. • Champion the design and development of secure, performant, and well-documented data APIs to facilitate data consumption across various applications and user groups. • Enforce data governance policies, standards, and procedures to ensure data integrity, security, privacy, and compliance. • Collaborate closely with data science and analytics teams to identify opportunities where data science and machine learning can be applied to optimize internal operations, automate processes, and improve efficiency within the data platform itself (e.g., predictive maintenance for pipelines, intelligent resource allocation). • Ensure the data platform is highly performant, scalable, and resilient, capable of handling growing data volumes and complex analytical workloads. • Evaluate and recommend new data technologies, tools, and platforms to enhance our data capabilities and stay ahead of industry trends. • Partner effectively with engineering, product, analytics, data science, and business teams to understand data requirements and deliver impactful solutions. • Establish robust monitoring, alerting, and on-call support processes for all data systems, ensuring high availability and rapid issue resolution.

🎯 Requirements

• 10+ years of experience in data engineering roles, with at least 5 years in a leadership or management position overseeing data engineering teams. • Proven track record of building and scaling complex data platforms from the ground up, or significantly evolving existing ones. • Deep expertise in designing, building, and operating: • Data Ingestion Pipelines: (e.g., Kafka, Flink, Spark Streaming, Airflow, equivalent cloud services like Kinesis, Pub/Sub, Dataflow) • Operational Data Stores: (e.g., Cassandra, ScyllaDB, DynamoDB, PostgreSQL, MySQL) • Data Warehousing/Lakehouse Technologies: (e.g., AWS, GCP, S3, Iceberg, Redshift, BigQuery) • Data APIs & Services: (e.g., RESTful APIs, GraphQL) • Strong proficiency in Java / Scala • Extensive experience with cloud data platforms (AWS, GCP) and their respective data services. • Solid understanding of data modeling techniques (relational, dimensional, NoSQL). • Literacy in Data Science and Machine Learning concepts: • Familiarity with common ML algorithms and their applications. • Understanding of the MLOps lifecycle and data requirements for ML models. • Ability to identify and articulate how data science/ML can be used to improve data platform operations (e.g., anomaly detection in pipelines, resource optimization). • Experience with implementing data governance, data quality, and metadata management tools and practices. • Excellent communication, interpersonal, and presentation skills, with the ability to articulate complex technical concepts to both technical and non-technical audiences. • Strong analytical and problem-solving abilities, with a focus on delivering practical and scalable solutions. • Bachelor's or Master's degree in Computer Science, Data Engineering, or a related quantitative field.

🏖️ Benefits

• Health insurance-employee premium paid 100% by Revinate • Dental insurance-employee and dependents’ premium paid 100% by Revinate • Vision insurance-employee and dependents’ premium paid 100% by Revinate • 401(k) with employer match • Short & Long Term Disability insurance • Life insurance • Paid Flex time off • Monthly work from home stipend • Telehealth access • Employee Assistance Program (EAP)