Druva, the autonomous data security company, puts data security on autopilot with a 100% SaaS, fully managed platform to secure and recover data from all threats. The Druva Data Security Cloud ensures the availability, confidentiality, and fidelity of data - providing customers with autonomous protection, rapid incident response, and guaranteed data recovery. The company is trusted by its more than 6,000 customers, including 65 of the Fortune 500, to defend business data in today’s ever-connected world. Amidst a rapidly evolving security landscape, Druva offers a $10 million Data Resiliency Guarantee ensuring customer data is protected and secured against every cyber threat. Visit druva.com and follow us on LinkedIn, X and Facebook.
Druva has raised over $350m in venture capital, is trusted by over 4,000 global organizations and protects over 200 PB of data.
Staff Data/Analytics/ML Engineer
The Role & The Team:
The Business Intelligence team is responsible for all data driven insights and activations of those data sets into operational cadence and business process optimizations for Druva. We are seeking a Staff Data Analytics/ML Engineer that will lead data to insights recommendation engine and stakeholder engagement. This is a high impact role that collaborates with highly skilled engineers & product teams and leverage industry leading data stack/tools.
What You’ll Do:
- Help bridge the gap between data engineering, analytics engineering, and machine learning to drive impactful business insights.
- Collaborate cross-functionally with Product, Engineering, GTM, and Customer Success teams to develop data-driven solutions that improve decision-making and business outcomes.
- Design, build, and maintain scalable data pipelines and infrastructure to support analytics, machine learning, and operational workflows.
- Develop and optimize data models for analytics and reporting, ensuring efficient storage, retrieval, and transformation of large datasets.
- Work on feature engineering, model training, and deployment pipelines to enable real-time and batch ML/AI solutions.
- Lead and contribute to architectural discussions, improving data governance, observability, and scalability across platforms.
- Evaluate and implement data tools and platforms that enhance data workflows, including Reverse ETL, MLOps, and DataOps frameworks.
- Provide mentorship and guidance to data engineers, analysts, and machine learning practitioners, fostering a culture of collaboration and technical excellence.
- Work closely with stakeholders to translate business needs into technical requirements, prioritizing projects that maximize impact and efficiency.
What We Are Looking For:
- Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, Statistics, or a related field.
- 7+ years of experience in data engineering, analytics engineering, or machine learning, with a strong technical foundation across all three domains.
- Expertise in modern data stacks, including Snowflake, dbt, Airflow, Spark, and cloud platforms (AWS, GCP, Azure).
- Experience with BI and analytics tools such as Looker, Sigma, Tableau, Power BI, or similar.
- Strong experience in SQL, Python, and distributed data processing frameworks (Spark, Dask, or similar).
- Experience building data pipelines, ETL/ELT processes, and data transformations for analytical and ML use cases.
- Familiarity with machine learning frameworks such as TensorFlow, PyTorch, Scikit-learn, and ML lifecycle management (MLOps).
- Deep understanding of data modeling, data architecture, and data governance best practices.
- Strong problem-solving skills with the ability to take ambiguous business challenges and design robust data solutions.
- Experience working in Agile environments, prioritizing technical initiatives, and collaborating with engineering teams.
- Excellent communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.
Bonus Points For:
- Experience with Generative AI/LLMs and integrating AI solutions into business workflows.
- Knowledge of streaming architectures (Kafka, Kinesis, or Pub/Sub) and real-time analytics.
- Experience with Data Contracts, Data Quality frameworks, or Data Mesh architecture.
- Familiarity with Reverse ETL tools like Salesforce, Census, Hightouch, or Segment for operational analytics.
