Top Tech Jobs & Startup Jobs

Prepare and analyze financial data, build models for short- and long-term forecasts, support budgeting and month-end close activities, investigate variances, assist accounting with journal entries and accruals, and produce presentations for leadership and internal stakeholders.

Lead a data engineering team to develop and maintain data warehousing solutions, manage legacy integrations, guide architecture and vendor partners, deliver projects using Agile, monitor data quality and platform health, and engage stakeholders to align technical work with business needs.

Lead pod-level product delivery: own and maintain backlogs, translate initiatives into detailed PBIs with acceptance criteria, validate releases, coordinate stakeholders and cross-functional teams, support enterprise programs, manage enhancements, monitor capacity and budget impacts, document in approved tools, and drive AI/automation to improve productivity.

Lead design and implementation of enterprise, cloud-native data architectures supporting analytics and AI/ML. Develop standards, data models, integrations, and modernization strategies; partner with vendors; ensure governance, security, observability, and scalable real-time pipelines.

Provide forward-looking revenue analysis and predictive insights to identify revenue-maximizing opportunities. Partner with Revenue Management to build models, streamline analytics workflows, create BI visualizations, and deliver clear strategic recommendations to executive stakeholders. Lead ad-hoc deep dives, ensure data integrity, and automate insight delivery to accelerate commercial decision-making.

Design, build, and validate predictive models and forecasting algorithms for pricing, booking curves, cancellations, and upgrades. Prepare and ETL large datasets (Snowflake) using SQL and Python, calibrate RMS algorithms, run A/B tests and attribution, and create dashboards in Power BI or Tableau. Collaborate with data engineers and revenue managers to productionize models and translate results for stakeholders.

Design, build, and maintain cloud-based data pipelines and ETL into data warehouses (Snowflake/AWS). Ensure data quality, security, and performance; optimize storage and retrieval; automate tasks with Python/Bash; monitor/troubleshoot pipelines; mentor junior engineers; evaluate new technologies and support legacy systems.