Druva is the leading provider of data security solutions, empowering customers to secure and recover their data from all threats. The Druva Data Security Cloud is a fully managed SaaS solution offering air-gapped and immutable data protection across cloud, on-premises, and edge environments. By centralizing data protection, Druva enhances traditional security measures and enables faster incident response, effective cyber remediation, and robust data governance. Trusted by nearly 7,500 customers, including 75 of the Fortune 500, Druva safeguards business data in an increasingly interconnected world. 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 EngineerThe 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.
- 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.
- 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.
What We Do
Druva delivers data protection and management for the cloud era. Druva Cloud Platform is built on AWS and offered as-a-Service; customers drive down costs by over 50 percent by freeing themselves from the burden of unnecessary hardware, capacity planning, and software management.
Why Work With Us
We are the leader in cloud data protection and cloud is the way of the future! With over $300M in funding and our Pre-IPO status, it is the perfect time to jump on board. Two of our company values are "challenger mentality" and "one team". We truly believe in the impact we can make together and we are not afraid to push the status quo.
Gallery
