ABCDEFGHIJKLMNOPQRSTUVWXYZ
1
What questions do you have for our guest speaker?
2
This is really dependent on what the topics your study covered and how much independent work you've done to learn some the more modern tools used by companies on your own time. The upside of the changes in the industry in the last decade is a lot of the tools are open source or have some open source version that you can use without paying any expensive licenses as a student.Do we need pursue a master's degree for Data Engineer/Data Scientist roles at Databricks?
Currently, Databrick's data science intern require master's degree. What can we do as undergraduate data science students do to break these barriers? What sort of other qualification Databricks looking for in a data science/data engineer intern? I felt like companies valued a master's degree rather than an undergraduate.
3
You have to want to keep optimizing. New technologies come out all the time that does one thing or another better than its predecessor. It's important to care how technology improves and take some time test new tech out.Would love to hear about her experience as a developer and some of the key lessons she learned in industry to continue improving
Also what is it that keeps her motivated to keep improving
4
Attached some blog posts. CDC use case is bread and butter I would take a look.Could you provide insights into the key strategies and best practices for successfully implementing Data Engineering on the Lakehouse Architecture using Databricks and Delta Lake?
5
I was a Customer Data Scientist or Sales Engineer at H2O and Databricks needed more DS/ML expertise because the company was so Data Engineering and SQL focused. I went where I saw there was a need and I think that's the best way to pivot in your career is to find ways you can best contribute. Demand meets supply and everyone wins.How did you get into working for Databricks?
6
Current one, hopefully if you're doing well in your career you pick what you want to do next. This coming from the perspective that doing well means doing what you like.Favorite job position?
7
This one is easy for me because my customers constantly ask me about new technology and comparison of them so it's literally my homework to read/test/summarize on new tech and software findings. Usually what's hard once you have your first job is to have to time to do this, for me it's easier because it's baked into my job hours.what resources do you you suggest to stay up to date with technology?
8
The more case studies you see the better. Case studies doesn't just go over how a technology works but what the business justification is. It's not technology for technology's sake. Often time this means engineering blogs from companies with a strong Digital footprint and strong data eng team. This includes companies like Doordash, Uber, Grammarly, Coinbase, Samsara, Rivian, etc. These companies need to know how to build large systems and often ahead of the game because they have the most high volume data with the most complexicity.What resources do you recommend student self-teach themselves for industry?
9
Feeling like you're falling behind constantly because you can't know everything. You have to learn to be at piece with being a fool at most things and be confident in asking all the stupid questions. One dumb question today hopefully means ten smart decisions tomorrow.What do you find the most challenging working in a such fast pace industry? And how did you overcome those challenges?
10
If I charted what I know today and wanted to optimize on compensation I would say go into software engineering if you can. But I chose the best opportunity that I had at any moment and didn't have any grand plans. Being able to pivot affords you a lot more opportunities to find what you enjoy and can be great at. I liked Math > CS so I studied Maths, then I enjoyed talking to customers and solving business problems > QA-ing product and writing product code, etc and it sort of just lead me to my current role.Since data science was not as big as a field as it is today, how did you know at the time that that was the field you wanted to enter?
11
I will attach some in this spreadsheet.Where do you get case studies to read from?
12
See attached resources.Where can we submit our resume to databricks and learn more about becoming a data engineer?
13
The specifics really depends on the role. SA vs DE/DS is very different. Some of the guiding principles at Databricks that's core to Databricks culture are: Customer obsession, Transparency, and acting on first principals. Other then technical qualifications we tend to look for folks who continue to put the customer first and build a customer first product. Being thoughtful and designing solutions with edge cases and consequences in mind and making sure you're transparency about what worked and what didn't.What advice would you give to someone applying for a role at databricks?
14
I remember very little about my courses in Numerical analysis except that it taught me to solve problems in a more practical fashion rather than theoretical. You can't have the perfect solution everytime but you want to get creative about solving a problem that doesn't have a predesigned perfect solution.How does having an applied math background concretely benefit your work?
15
Definitely and much easier if you already interned somewhere.Do undergrads typically find data engineering positions after graduation?
16
Breadth of use cases at Databricks. Product is multi faceted because Databricks' customer problems are multifaceted.What did you find the most novel about working at Databricks compared to other companies?
17
That's why I'm a solutions architect, every customer is different and you have to be a little bit of a DE, DS, DA, systems admin, etc.Do you think the work of a Data Engineer ever gets too repetitive?
18
Solving and unblocking customer problems. Each is like a little puzzle and it's really satisfying to know solving a puzzle meant $1million savings or 20% increase in user acquistion.what is your favorite aspect of your job?
19
More stern now than it was when I first joined because it's a bigger company now but manager typically don't discourage flexing between teams or disciplines. It was much easier to flex between jobs when a company is smaller and you're basically overworked.How fixed is the team structure within Databricks / What are the challenges of switching from analyst/engineer to data scientist (how do you get the data science work experience when you are in a different position)
20
Emerging tech that I see over and over: LLMs, ChatGPT, Delta, DBT.
Estasblished tech I deal with on a day to day basis: Spark, Delta, Airflow, MLFlow
What are some emerging trends or technologies in the solutions architect landscape or the data management field that you think will have a significant impact in the near future?
21
Start in one role and start over. If you're not afraid of starting over that's going to be the biggest help. Otherwise typically if you're doing your best at a job you're probably trying to understand things from a larger scope and that zooming out of your project usually leads to understanding upstream or downstream tech.How easy it is to switch between data analyst/scientist/engineer roles? What resources are best for learning the new skills/technology necessary for each of these roles?
22
what has been the most challenging project you have worked on?
23
First principals: you have to start core requirements for how data cannot be used, shared, or exported and work from there. This often involves understanding what's the biggest way to isolate the data and working backwards to create secure processes to share with specific people or teams. Recommend reading some of the blog posts on data exfiltration and unity catalog.How do you ensure there is no data leaks
24
Work life balance is hard. I had none for many years so I don't think I am best person to answer this question. I have kids now so it's a forcing function to work less.How do you prepare yourself during your transition through various roles? How do you balance your work-life balance, following what you feel passionate about, and prepping for the future?
25
Hard to say there's luck involved in everything. Don't just look for one role. Spread your search to different data roles and see where you find syngery with folks interviewing you. Customer support engineers, and solutions architect at data companies are actually data related roles which most undergrad wouldn't search by.How do we make ourselves the one starting point to break in is it more focusing on time spent on resumes or networking or other areas?
26
This comes hand in hand with my existing role and you just end up talking to many folks because you want to collaborate as much as possible to arrive at the best solution. Usually if you don't mind asking questions and the company culture is good then folks don't mind answering your questions.In your talk you mentioned the need to speak to many different people (product managers, data scientists, business analysts, etc.), do you have any advice on how to get familiar with the verbiage and tools across all these different inter-related spheres?
27
I would say internships are better to start with and easier to get than a committed full time position.What are some of your tips for the recruiting process for a new grad position at Databricks?
28
Money. A lot of decisions have to be made based on efficiency and accuracy because at the end of the day you have to calculate how much you spend to develop a product and how that product brings in value.what did you feel is the most different between academia and industry
29
Going from near real time analytics to real time analytics. Solutions like Rockset are interesting to look out for. Are there any emerging trends or technologies in the data engineering and analytics space that you find particularly exciting or promising for the future?
30
Went straight into being a DS at Department of Transportation and Vendavo. Met H2O's CEO at Vendavo and meetup and moved to work at H2O instead.What were your experiences right after college?
31
Read lots of documentation and try stitching tech stacks like Kafka->Spark->Delta. Building demos that showcase what you learned and educating other people. Help customers design something similar on their data in an optimal way.What does a solutions architect do on a day-to-day basis?
32