Engineering Full-Time

Data Analyst (US)

Candid Health • San Francisco, CA

Company

Candid Health

Annual salary

$135k – $200k/yr

Location

San Francisco, CA

Listed

73d ago

Experience:: 4 to 8 years
Workplace:: 4 days per week in-office (San Francisco, New York, or Denver) with 1 day remote flexibility
Visa:: Visa sponsorship available
Equity:: Competitive equity

Hybrid in San Francisco, CA. Visa sponsorship available.

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About this Role

At Candid Health, we’re searching for a Data Analyst to help us enhance efficiency, product scalability and operational decision making. You will perform critical analysis and implement data driven solutions that enable our engineering, product and leadership teams to deliver transformative software to the healthcare providers.

What you'll do

Perform Critical Data Analysis for a Broad Range of Stakeholders: Build analytics, and visualizations for varying use cases. Play an influential role in delivering the insights that Candid Health uses to inform our business strategies
**Enable Self Serve Solutions to Internal Teams: **Deliver internal products that make our teams more more efficient and self reliant through improved tooling and operational workflows
**Build High Value Data Products: **Data Analysis is in the critical path of value for Candid Customers. Build robust, scalable data models for Candid clients.
**Own BI Platform Implementation: **Maintain reporting and visualization platforms to keep data accurate, accessible, and available

What you need

You have Bachelors degree in Math, Science, Engineering, or another data intensive field of study such as Library Science
You have 4+ years of experience working with data at scale, performing analysis, ETL, and process automation
You have 3+ years of hands on experience working with common data warehouses such as Snowflake, BigQuery, or Redshift
You have a strong understanding of SQL and have used it in complex data environments
You have a deep understanding of how different users use data and are able to build the right insights and tools for them
You thrive in ambiguity and enjoy tackling complex tasks that have significant impact on the business at large
You’re a clear and concise communicator; you enjoy the challenge of explaining complicated ideas in simple terms, both in-person and in writing.
Experience or exposure with the broader set of technologies in our ecosystem: Google Cloud Platform (BigQuery), Metabase, Terraform, Python, DBT.

Frequently Asked Questions

How do I apply for the Data Analyst (US) role at Candid Health? +

Use the Apply for a referral button on this page to send us your LinkedIn and CV. If your experience fits, we'll introduce you to the recruiter filling this role. They'll email you to check you're interested, then put you forward for this job or others that suit you better. It's free.

What does this Candid Health role pay? +

The listing gives $135k – $200k/yr.

Is this role remote? +

The listing gives the location as San Francisco, CA. 4 days per week in-office (San Francisco, New York, or Denver) with 1 day remote flexibility. Visa sponsorship available.

Interview Prep

Sample questions for a Data Analyst role, written in-house to help you prepare.

What's your process for cleaning a dataset before starting analysis?

I start by profiling the data for missing values, duplicates, and outliers, then decide case by case whether to impute, drop, or flag issues based on how they'd affect the specific analysis. I document every transformation so the cleaning process is reproducible and reviewable, not just a black box.

How do you decide which statistical test is appropriate for comparing two groups?

I check the distribution of the data and whether the groups are independent or paired first, since that determines whether a t-test, a non-parametric alternative like the Mann-Whitney U test, or a paired test is appropriate. Choosing a test based on convention rather than the data's actual properties leads to unreliable conclusions.

What makes a data visualization misleading, even if the underlying numbers are correct?

Common issues include truncated axes that exaggerate differences, using pie charts for data with too many categories to compare visually, or choosing a chart type that implies a trend the data doesn't actually support. I try to match the chart type to the specific comparison I want the viewer to make.

How do you handle outliers in a dataset when you're not sure if they represent errors or real extreme values?

I investigate the source of the outlier before deciding how to handle it, since a data entry error should be corrected or removed, while a genuine extreme value, like a real high-value transaction, should usually stay in the dataset. Removing outliers automatically without checking their origin risks losing real signal.

See all 10 questions for this role →