
Artificial Intelligence • Enterprise • SaaS
Monte Carlo is a data observability company that provides a comprehensive platform to ensure reliable data and AI delivery. Their services include machine learning-enabled data anomaly detection, incident triaging, and root cause resolution to maintain high data quality. They offer data quality monitoring, testing, and cost optimization tools for cloud infrastructures, enabling companies to deploy and manage data with confidence. Monte Carlo is tailored for verticals such as financial services, healthcare, media, and technology, helping organizations to self-serve, maintain dashboard integrity, and launch reliable data products. Their solutions aim to eliminate data downtime and enhance operational efficiency across enterprise data landscapes.
October 16

Artificial Intelligence • Enterprise • SaaS
Monte Carlo is a data observability company that provides a comprehensive platform to ensure reliable data and AI delivery. Their services include machine learning-enabled data anomaly detection, incident triaging, and root cause resolution to maintain high data quality. They offer data quality monitoring, testing, and cost optimization tools for cloud infrastructures, enabling companies to deploy and manage data with confidence. Monte Carlo is tailored for verticals such as financial services, healthcare, media, and technology, helping organizations to self-serve, maintain dashboard integrity, and launch reliable data products. Their solutions aim to eliminate data downtime and enhance operational efficiency across enterprise data landscapes.
• Own the technical relationship for some of Monte Carlo’s largest enterprise customers, guiding them through onboarding, integration, and ongoing success. • Partner with customer’s data engineering, analytics, and platform teams to embed Monte Carlo into their data ecosystem (e.g., Snowflake, Databricks, BigQuery, Redshift, dbt, Airflow, Kafka). • Help customers troubleshoot and understand complex data observability and pipeline challenges, collaborating closely with Monte Carlo’s Product, Support, and Engineering teams. • Serve as a trusted advisor, guiding customers on data & AI observability best practices that deliver measurable business impact. • Lead technical workshops, health checks, and account reviews to ensure long-term adoption and customer success. • Act as the voice of the customer, surfacing insights and feedback to shape Monte Carlo’s roadmap. • Partner closely with Sales Engineers and Solution Architects to design and proactively recommend strategies that help customers achieve their business objectives. • Build a deep understanding of each customer’s environment, use cases, and challenges to deliver expert technical guidance and support throughout their journey. • Create and execute a structured plan to drive efficient and optimized monitors across customer workloads.
• 5+ years in a customer-facing technical role such as Technical Account Manager, Solutions Architect, or Technical Customer Success Manager. • Understanding of modern data technologies — SQL and the major data warehouses (Snowflake, BigQuery, Redshift, Databricks) or orchestration tool (Airflow, dbt). • Strong project management skills to drive success across large, complex enterprises. • Familiarity with data & AI observability, monitoring, or observability concepts — or a strong interest in learning. • Exposure to cloud infrastructure (AWS, GCP, or Azure) and APIs. • Excellent communication and storytelling skills — able to explain technical topics clearly to both engineers and executives. • A proactive, problem-solving mindset with the ability to juggle multiple priorities in a fast-paced, customer-first environment. • Nice to Have: Previous experience in data & AI observability, monitoring, data technology, or analytics SaaS. • Background as a data engineer or analytics lead before moving into a customer-facing role. • Knowledge of data governance, cataloging, or compliance frameworks. • Familiarity with Python, SQL, and/or scripting is a plus — or a willingness to learn quickly in these areas.
• Competitive compensation, meaningful equity, and comprehensive benefits. • Flexible remote work, generous PTO, and a culture built on autonomy and trust.
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