Staff Product Manager, Disaster Recovery

Posted 8 Days Ago
Be an Early Applicant
Seattle, WA
150K-266K Annually
Senior level
Big Data • Software
The Role
As a Staff Product Manager for Disaster Recovery, you will define the product vision and strategy while collaborating with stakeholders to develop product offerings. You will conduct market research, translate customer needs into product specifications, launch new features, and analyze product performance.
Summary Generated by Built In

RDQ424R175

Our mission at Databricks is to be the generational Data and AI company. Databricks has created a new market category of the Data Lakehouse (CIDR 2021 paper), an open platform that unifies data warehousing, ELT, advanced analytics, and AI. The Lakehouse architecture addresses several major challenges, including data staleness, reliability, total cost of ownership, data lock-in, and limited use-case support. 

This is a unique opportunity to spearhead the inception of an innovative product line and have a huge impact on the entire lakehouse ecosystem. We are looking for a seasoned product visionary who can set the strategic direction and rally multiple stakeholders to actualize an unmatched multi-cloud lakehouse. Intrigued? Let’s chat! 

What is it like to be a PM at Databricks?

  • What three new PMs say about their first year at Databricks
  • Women in Product at Databricks
  • Why Databricks?

What we look for 

  • 7+ years of product management experience
  • Experience with crafting and building lucrative products with direct revenue from conception to launch
  • Experience in recognizing customer needs, creating an encapsulating product vision,  translating requirements to product specifications, driving alignment with stakeholders, building and launching products, and driving adoption post-launch 
  • Strong bias to ship products by understanding the most critical aspects to make the product successful
  • Familiarity with basics of databases such as  data structures and algorithms, row and columnar stores, networking, query optimization, and execution.
  • Bachelor's Degree in Computer Science or a related field
  • Nice to have: Hands-on experience with some of the data analytics products such as Postgres, MySQL, Oracle Exadata, Teradata, Netezza, Greenplum, Amazon S3, Redshift, Athena, Glue, Glacier, Azure Data Lake Storage (ADLS), Google Cloud Storage (GCS), Delta Lake, Data Lakes, Google BigQuery, Azure Synapse, Snowflake, Apache Hudi, Apache Iceberg

Responsibilities

  • Architect the product vision, strategy and roadmap for a multi-million $ revenue product line, aligning with the company's goals and priorities.
  • Conduct market research and user feedback to identify customer needs and pain points related to database technologies
  • Write clear and detailed product specifications, user stories and acceptance criteria for the database system technologies.
  • Partner with engineering, design, marketing and sales teams to plan, execute and launch new features and enhancements for the product line
  • Monitor and analyze the performance, usage and feedback of the database system, and use data-driven insights to inform product decisions and improvements.
  • Work with internal and external stakeholders, including senior management, investors, partners and customers, about the value proposition, benefits and status of the product.

Pay Range Transparency

Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents base salary range for non-commissionable roles or on-target earnings for commissionable roles.  Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location. Based on the factors above, Databricks utilizes the full width of the range. The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above. For more information regarding which range your location is in visit our page here.


Zone 2 Pay Range

$132,700$234,800 USD

About Databricks

Databricks is the data and AI company. More than 10,000 organizations worldwide — including Comcast, Condé Nast, Grammarly, and over 50% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark™, Delta Lake and MLflow. To learn more, follow Databricks on Twitter, LinkedIn and Facebook.
Benefits
At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. For specific details on the benefits offered in your region, please visit https://www.mybenefitsnow.com/databricks. 

Our Commitment to Diversity and Inclusion

At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affiliation, race, religion, sexual orientation, socio-economic status, veteran status, and other protected characteristics.

Compliance

If access to export-controlled technology or source code is required for performance of job duties, it is within Employer's discretion whether to apply for a U.S. government license for such positions, and Employer may decline to proceed with an applicant on this basis alone.

The Company
Seattle, WA
0 Employees
Hybrid Workplace
Year Founded: 2013

What We Do

As the leader in Unified Data Analytics, Databricks helps organizations make all their data ready for analytics, empower data science and data-driven decisions across the organization, and rapidly adopt machine learning to outpace the competition.

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