Senior Manager, Data Engineering

Accountabilities:: Lead and guide engineering teams across data engineering, enterprise data applications, and machine learning, providing technical direction, delivery oversight, and people leadership. Build and operationalize end-to-end data pipeline architecture, including ingestion and transformation layers that move CRM and ERP data into centralized platforms such as Snowflake or Databricks. Partner with Finance, Sales, and Operations to develop unified reporting models covering pipeline health, recurring revenue, billing, churn, and customer lifetime value. Establish and enforce data governance and analytics engineering standards, including version control, automated testing, modular data modeling, and reliable single-source-of-truth practices. Act as a trusted technical advisor to senior and executive stakeholders, translating complex financial, operational, and technical metrics into clear business insights supported by reliable data. Collaborate with Staff and Principal Engineers to influence architecture, define engineering best practices, improve product quality, and deliver scalable features. Provide technical leadership across ML engineering and data science initiatives, ensuring models move from experimentation into reliable production systems rather than remaining one-off analyses. Set standards for feature engineering, model training, evaluation, serving, monitoring, rollback, and feedback loops that maintain model performance as product and threat patterns evolve. Partner with Product, Platform, and Security teams to translate identity, device, and telemetry signals into measurable outcomes such as precision, false-positive rates, and time-to-detect. Establish appropriate SLAs, monitoring, on-call practices, and operational processes for data and model health. Hire, onboard, coach, mentor, and manage a growing team, while supporting performance development and career progression across multiple levels of individual contributors. Lead teams effectively in a geographically distributed, remote-first environment and contribute to building a collaborative, high-performing engineering organization. Drive continuous improvement through innovation, process development, delivery excellence, reliability, and quality initiatives. Encourage effective use of AI coding agents and productivity tools to improve engineering workflows and maximize team effectiveness. Requirements: 8+ years of experience in applied machine learning, including several years of people management and experience operating production MLOps environments with versioned training pipelines, evaluation gates, model registries, and automated promotion to serving. Proven experience managing teams of 8 or more engineers or technical professionals, including performance management, hiring, mentoring, and building high-performing teams. Strong experience with data and analytics technologies such as Salesforce, NetSuite, dbt, Fivetran, Snowflake, and Databricks. Strong SQL skills and a solid understanding of software engineering principles, architecture, reliability, and production operations. Hands-on fluency in Python and experience with machine learning frameworks such as scikit-learn, PyTorch, or TensorFlow, alongside large-scale data platforms such as Spark, Snowflake, or equivalent technologies. Experience leading ML engineers and data scientists who deploy models into production and manage the complete lifecycle from feature engineering through monitoring and feedback. Strong understanding of statistical and machine learning concepts, including supervised and unsupervised learning, anomaly detection, classification, ranking/scoring, model evaluation, and imbalanced datasets. Demonstrated ownership of SaaS products or platforms, with a strong focus on reliability, operational excellence, and sustainable engineering practices. Experience working with agile teams and collaborating effectively with engineering managers, technical specialists, and non-technical business stakeholders. Proven ability to lead geographically distributed teams and operate successfully in a remote-first environment. Strong communication, stakeholder management, coaching, and organizational skills, with the ability to operate effectively in a fast-moving and collaborative environment. Experience with AI-assisted development tools such as Cursor, Claude, or Copilot, as well as productivity tools such as Gemini or NotebookLM, and the ability to apply them effectively to day-to-day work. Fluency in written and spoken English is required. Candidates must be located in and authorized to work in India. Benefits: Competitive senior-level compensation aligned with experience and market expectations in India. Fully remote work within India as part of a remote-first working environment. Opportunity to lead and grow high-performing, geographically distributed engineering teams. Significant influence over engineering practices, architecture, product strategy, and execution. Opportunity to work on complex data, machine learning, identity, device, and telemetry systems at scale. Strong exposure to senior leadership and cross-functional collaboration across Product, Platform, Security, Finance, Sales, and Operations. Career growth opportunities through technical leadership, people management, and organizational impact. Collaborative environment that values innovation, continuous improvement, diverse perspectives, and strong human connections. Opportunity to incorporate modern AI tools into engineering and productivity workflows. Participation in engineering on-call rotations as part of maintaining reliable production systems.