Experience and Opportunities at Databricks
Topic: Experience and Opportunities at Databricks
Presenter: Yunbo Deng
Career Experience and Opportunities at Databricks
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Experience and Opportunities at
Prior personal experience in Google
Like to connect with great engineers and talents
Grow career
Bio
Databricks: partner integration, ecosystem, DBSQL
Google: ads
Microsoft: exchange
Companies
MS: established company, good reputation. Down level
Google: rising star. Down level
Databricks:
high growth.
1000+ engineers
Other business oriented teams
Pre IPO
Promising, track record
Engineering centered. Inspired by Google culture
Databricks
Spark
Data + AI. Acquired Mosaic: AI/LLM tooling
Lakehouse = Database + Warehouse
Products: notebook, jobs, ML workflow, DBSQL, Unity Catalog (data governance,e.g. permission), AI (mosaic)
Stats
Pre-IPO
Revenue ~$2B
Snowflake, microsoft, Google
Eng culture
Big companies
Predictable, processes
Perf review: ownership/identity
Organization support
Small companies
Less formality
Personal outreach & org support
People relations are more important - horizontally reachout
Take ownership
Hiring & interview
Big companies
Recruiting
Technical interview & HM interview
Team match
Standard offer
Small companies
Hiring manager, influence interview set up
HM needs to collect feedback and write support statement
Committee review
HM prepare offer and close
Audience
HM is more influential, can we start one round of chat with HM?
Speaker
Yes. HM round is standard
Submit resume online -> processed by a rotation
More ideal is to ping HM first. Much more efficient
Hiring and interview
Great experience
Resilience and perseverance
(job hopping is minus. Redflag: 3 year 2 jobs)
Dealing with org change gracefully
Nail interviews - must prepare
Growth trajectory (expect leveling up at reasonable speed)
References (backdoor reference - people may know you)
Culture fit
Look up company culture
Founder story
Truth seeking, bias towards action, data driven, customer obsession, collaboration, communication, passion and enthusiasm, growth mindset/self reflection, relevant experience
Smart, bold, hard working (*), humility
Hard Work makes difference
Humility:
difficulty/mis-steps, how do you solve it?
I have a higher goal
Mistake - it’s ok. Accept.
Eng work
Typical
Quarterly planning
Change is ok
Prioritization / trade-offs
Intensity
Eng culture
Eng driven
Velocity vs stability, hacking vs solid engineering
Growth in size:
Diverse background
Can influence the culture
Doing right things in the right eng way - first principles
Career Growth
Review
Inspired by google
Lightweight for IC, more work for managers
Evolving
Mobility
You can work on projects in other teams
Expectations
Need self advocate. Long run is better for growth.
Better than relying only on manager
Controlling your own future
Will tell you deficiency
Misunderstanding: growth fast.
Truth: based on impact
Career growth: justified by impact but senior leaders can have huge influence
Level:
Similar to Google and Meta
Comp
Salary, bonus and equity (RSA). pre-IPO RSA not taxed
Seattle site
Bellevue (biggest in seattle area)
Partner organization, cloud infra, cluster mgmt, marketplace, delta sharing, test foundation, notebook
Seattle
Often can work on remote projects
Q&A
Audience:
What does your team do?
Speaker
Work with partners (other companies) to build the best databricks experience
Audience
Do you support research?
Statistics background ?
Speaker
Data science team
Research: blended into eng org. Some work in ML/model training/machine learning. Engineer title.
Audience
LLM, GenAI. Plan?
Speaker
GenAI is used to generate queries
Catalog explorer - navigate hierarchy. Table: contains comments to describe the table.
Query: autocomplete query.
Planning + hackathon
Company does not sell or train models. It produces tools for compute resource mgmt. Make AI training more affordable
Audience
Infrastructure compared to other companies
Speaker
We are not worried that Microsoft, AWS, Google will deliver data infrastructure well
Databricks provides control plane
Spark engine - java engine
Databricks - photon engine, c++. 5x-6x speedup compared to spark
Audience
I worked on hardware. Is it a useful experience?
Speaker
Pays a lot of attention to resume and background relevance
More relevant the better.
General software engineers can also work.
Try to find a reference. Try to customize to the requirements of this company.