CSQ227R13
About DatabricksDatabricks is The data and AI company. More than 10,000 organizations worldwide rely on the Databricks Data Intelligence Platform to unify data, analytics, and AI. Founded by the original creators of Lakehouse, Apache Spark™, Delta Lake, and MLflow, Databricks is building the foundation for organizations to put data and AI to work.
The impact you’ll haveAs a Senior member of the Learning Platforms team, you will shape the technical direction of the systems that help employees, partners, and customers build the skills they need to succeed with data and AI. You will operate as a broad platform owner - moving fluidly from product strategy and architecture to the hands-on engineering, cross-functional alignment, and reliable operations.
You will lead the design and delivery of intelligent, data-driven learning experiences that connect content, skills, assessments, and business outcomes. This is a high-impact individual contributor role for an individual who can turn ambiguous business problems into secure, scalable products!
What you’ll do- Own the architecture and technical strategy for a portfolio of learning platforms and enablement products, from early discovery through production operation and evolution.
- Lead the design and implementation of AI-powered capabilities, including recommendation systems, skill inference, intelligent search, personalization, and analytics-driven learning workflows.
- Build secure, privacy-aware data systems that integrate information across learning, workforce, customer, and operational platforms.
- Develop Databricks-native applications, data pipelines, APIs, automations, and user experiences that make complex learning processes simple and scalable.
- Establish strong engineering practices for reliability, observability, testing, release management, access control, and lifecycle ownership.
- Make pragmatic decisions and define integration strategies across learning management systems, lab environments, content platforms, identity systems, and internal data products.
- Partner with business leaders to define product direction, prioritize roadmaps, clarify trade-offs, and deliver measurable outcomes.
- Navigate complex requirements involving security, privacy, compliance, governance, and responsible use of AI.
- Lead cross-functional technical programs involving multiple engineering teams and senior stakeholders, creating clarity and momentum in ambiguous environments.
- Mentor engineers, raise the technical bar, contribute to hiring, and create reusable patterns that help the broader organization move faster.
- Serve as a technical thought leader internally and externally through architecture documents, technical presentations, reusable guidance, and community engagement.
- 5-7 years of experience designing, building, and operating production software or data platforms, with a track record of leading work across organizational boundaries.
- Demonstrated experience owning architecture and technical direction for complex, multi-system products.
- Strong full-stack or platform engineering background, with depth in backend services, APIs, data pipelines, cloud infrastructure, and modern web applications.
- Experience designing and operating AI- or data-intensive systems, such as recommendation engines, inference pipelines, search, personalization, or analytics products.
- Fluency in Python or a similar production programming language, plus strong SQL and experience with relational and analytical data systems.
- Experience with cloud platforms, distributed systems, identity and access management, security controls, and production operations.
- Strong judgment around data privacy, governance, least-privilege access, and responsible AI.
- Ability to communicate clearly with engineers, product managers, security and legal partners, and senior business leaders.
- A track record of delivering results with minimal oversight while bringing structure to ambiguous problems.
- A collaborative leadership style that combines technical depth, product thinking, pragmatism, and a bias for action.
- Experience with Databricks products and technologies such as Databricks Apps, Lakehouse data architectures, Unity Catalog, Delta Lake, Lakebase, Workflows, SQL Warehouses, or the Databricks SDK.
- Experience integrating learning, content, assessment, identity, HR, or customer-facing platforms.
- Experience building systems that serve both internal users and external customers or partners.
- Experience scaling automation and operational workflows across large user populations or high-volume data sources.
- Experience with modern frontend frameworks such as React and application frameworks such as Flask or FastAPI.
- Experience mentoring senior engineers or leading technical programs across several teams.
- Evidence of technical thought leadership through conference talks, publications, open-source work, or industry communities.
The Learning Platforms team builds the digital ecosystem behind Databricks learning and enablement. We combine software engineering, data, AI, and product thinking to improve how people discover content, develop skills, practice with the platform, and measure progress. Our work spans learner experience, intelligent recommendations, platform integrations, operational automation, cost efficiency, and reliable delivery at scale.
Pay Range Transparency
Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected 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 anticipated utilizing 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.
About Databricks
Databricks is the Data and AI company. More than 20,000 organizations worldwide — including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500 — rely on the Databricks Data + AI Platform to build and scale data and AI apps, analytics and agents. Headquartered in San Francisco with 30+ offices around the globe, Databricks offers a unified platform that includes Genie, Lakebase, Agent Bricks, Lakeflow, Lakehouse, and Unity Catalog. To learn more, follow Databricks on LinkedIn, X, YouTube, and Instagram.
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 click here.
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.
Skills Required
- 5+ years of experience in applied ML or data science with production recommendation or personalization systems
- Hands-on experience with knowledge graphs, graph databases, or ontology design
- Experience with LLM APIs and prompt engineering for generative features
- History of shipping LLM-based systems to production, including large-scale deployment, evaluation frameworks, and agentic workflows
- Advanced Python proficiency and experience architecting robust, production-grade applications
- Deep familiarity with modern AI stack: retrieval and agent frameworks, prompt engineering, model evaluation, context engineering
- Exceptional communication skills to translate technical logic for varied stakeholders
- High degree of intellectual curiosity and ability to find elegant solutions
Databricks Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Databricks and has not been reviewed or approved by Databricks.
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Equity Value & Accessibility — Equity grants are a meaningful part of offers, and periodic tender opportunities and secondary options have made private equity more tangible for many employees. This perceived upside contributes to strong total-compensation sentiment in key roles.
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Healthcare Strength — Comprehensive medical, dental, and vision coverage is paired with mental‑health resources and wellness reimbursements, indicating a robust health package. Multiple summaries highlight broad coverage that employees can practically use.
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Leave & Time Off Breadth — Generous PTO, paid holidays/sick time, and fully paid parental leave are frequently described, with hybrid/remote flexibility common in the U.S. These policies expand time‑off accessibility across different life stages.
Databricks Insights
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. By providing data teams with the ability to process massive amounts of data in the Cloud and power AI with that data, Databricks helps organizations innovate faster and tackle challenges like treating chronic disease through faster drug discovery, improving energy efficiency, and protecting financial markets.
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