Senior Software Engineer, Bluesky

Data Cloud Optimization

$160-200k

+ Generous equity grant

SQL

Python

Snowflake

dbt

Senior level

San Francisco Bay Area

2-4 days a week in office (Menlo Park, CA)

Snowflake workload optimization and cost governance

Job no longer available

Snowflake workload optimization and cost governance

1-20 employees

B2BEnterpriseProductivityBig dataSaaSAutomation

Job no longer available

$160-200k

+ Generous equity grant

SQL

Python

Snowflake

dbt

Senior level

San Francisco Bay Area

2-4 days a week in office (Menlo Park, CA)

1-20 employees

B2BEnterpriseProductivityBig dataSaaSAutomation

Company mission

To help customers drive healthy cloud consumption, providing more visibility and optimization into the efficiency of workloads. To allow its customers to derive the most value from their Snowflake Data Cloud usage.

Job

Company

Role

Who you are

5+ years of software engineering with expertise in a data domain
Strong analytical skills with an understanding of query processing internals
Practical experience in data engineering, data warehousing or data science
Fluent in object-oriented programming and SQL optimization
Proven track record of leading large projects
Strong problem-solving skills and communication skills

Desirable

Hands on experience with Snowflake, DBT, Prefect or Airflow
Previous experience working in a startup environment
Research experience in data related field or databases

What the job involves

We're looking for a Founding Software Engineer passionate about tackling big data challenges in the next-generation data clouds
As a crucial early team member, you'll play a significant role in making important architectural decisions that will shape our technology stack for years to come
You'll also have the chance to make an immediate impact on the big data stack at multiple well-known tech and non-tech companies who are our customers
Design and build highly-efficient and scalable ETL pipelines with Snowflake, Airbyte, dbt, and Prefect
Build models of Snowflake query execution for estimating optimal warehouse sizes
Analyze query patterns/profiles to recommend storage optimizations like table clustering keys
Identify recurring workloads vs one-off workloads through query pattern analysis
Recommend query optimizations by recognizing query types (e.g., suggesting making a query incremental if it's part of a dbt model)

Application process

Intro call with Recruiter
Technical Screen 1
Technical Screen 2
Product Manager Interview
Executive Interview with CEO

Our take

Data-driven organizations are on the rise - as is the volume of data by which they’re driven, and many are turning to Snowflake to help manage this. The software’s sliding price scale, however, means operating costs can stack up fast. Bluesky is hoping to help organizations combat this by providing data cloud efficiency services. The idea is to make analytics and querying faster, and therefore cheaper, keeping cloud costs lean and complexity low.

It does this by trimming off practices that are costly but ineffective, then working out the inefficiency knots in the remaining high-performance but pricey ones. Ultimately, it hopes to develop a software that carries this out continuously and autonomously.

Interestingly enough, Bluesky doesn’t actually compete with Snowflake. The two are partners, with Snowflake funnelling customers over to Bluesky who provide a much-needed cost guardrail. Whilst Bluesky is just recently launched, it can expect this arrangement to drum up some substantial business: over two fifths of the Fortune 500 use Snowflake, and it’s growing fast in line with the pivot towards data-driven business. This neat symbiosis, in combination with Bluesky’s elegant product and strong first steps into the market, makes for some strong potential.

Steph

Company Specialist at Welcome to the Jungle

Company

Funding (1 round)

Sep 2022

$8.8m

SEED

Total funding: $8.8m

Company benefits

Top-tier health coverage
401(k)
Unlimited PTO

Company HQ

Menlo Park, CA

Leadership

Mingsheng Hong

(CEO)

Worked as a Field CTO at Vertica Systems, Chief Data Scientist at Hadapt, Tech Lead at Google, and then Head of Engineering for ML Runtime.

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