Let’s be honest. The last, great, and perfect third-normal form database is probably Contoso. Why? Because a few people built it as a sample platform on which to showcase a large software company’s products. As elegant as Contoso might be under the hood (we have not checked the Contoso indices recently), it’s not the real world. It’s not the big data world in which we and our clients live. Our data world is messy, grows increasingly amorphous by the month, and desperately needs skilled data wrangling.
If the phrase “data wrangler” trips your trigger, read on. If not, there is nothing to see here so move along.
If you know that “SQL” is not a typo rampant de-normalization is, um, normal…
If you have been a “high touch” consultant or client advocate in a software product or consulting company…
If “making it work” is even more important to you than “architectural perfection”….
If “Monte Carlo” is more than a beautiful gambling destination for the rich and famous…
If you’re driven to revolutionize the way companies think about their data and enterprise analytics…
…then there is much to see and do at Aginity.
To excel with Aginity you must crave the opportunity to have your “fingerprints” all over the company. We are small enough that there are no weeds in which to hide and every person’s contribution counts.
We are a software company, not a recruiting firm, body shop, or internal IT organization. We seek only full-time, salaried, passionate and committed colleagues.
Aginity corporate headquarters are in suburban Chicago and we strongly prefer local talent. But, our employees live across the eastern United States, and relocation may not be required. For this role we will consider people who live east of the Mississippi river.
Alex, I’ll take “Responsibilities” for $200.
- Can you spot patterns and dig into them to understand whether they’re meaningful?
- Do you see relationships between ideas and things that other people might miss?
- Can you see the possibilities that would exist if you just knew “one more thing”?
- Are you able to tell when something is “not quite right”?
- Do you tend to explain big concepts with specific examples, or roll something specific into something much broader?
You’re going to look at a lot of data in this role. You’re not going to be familiar with what all of it means. You’re expected to ask questions of people, do your own research, and apply your own experience, to understand the data in front of you. Then you’re going to need to dig in deeper – look for patterns and problems, hierarchies and relationships.
You’ll need to figure out how many times something is missing and how many times it’s not. And, of course, what it all means. You’re going to need to figure out how to clean data when it’s bad, or how to make data from two different systems look the same. You’re going to use tools to pull data, push data, and store data. And you’re going to document and communicate to others who need to understand what you’re seeing.
You’ll wear a lot of hats, you’ll look at a ton of data, but in the end you’re going to be the person who understands it, who can explain it, who can lead the charge to make that data even more valuable through analytics.
Knowledge, Skills and Credentials
- Two to seven years of experience writing SQL code for multi-terabyte systems.
- Knowledge of database architectures such as star schemas, data normalization and de-normalization.
- Knowledge of Hadoop Hive, Spark or Netezza
- Waterfall, SCRUM, and Agile methodology expertise
- Working with data wrangling applications like Trifacta, Data Meer or ETL applications like Informatica or DataStage
- Business intelligence reporting
- Worked with, in or around analytic applications like SAS, R or SPSS
- Worked with Scala, Python
- Worked with Zepellin or Jupyter notebooks
- Data quality, Data profiling, Data analytics
- Know who Kimball and Inmon are and why they matter
- Client-facing data warehouse consulting experience
- Understands KPIs and a common set of measures/metrics
- Understands when to use surrogate keys
- Know the difference between ELT and ETL