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Unlocking the Datavant Engineering Interview Process: Part 2

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March 9, 2023
 min

The Virtual Onsite

This is the second part of our series unpacking the Datavant Engineering interview process. Read Part 1. Want to apply? We’re hiring.

“Onsite” is something of a misnomer for an async/remote-first company because (of course) there’s no actual site to be on. Like many companies, Datavant became remote-first during the pandemic, but has since committed to staying that way for the foreseeable future.

Why remote-first? Lots of reasons:

  • We believe that allowing team members to work from their preferred location ultimately aligns better with our valuing of fewer rules and more individual responsibility.
  • Our teams and employees have enjoyed equal or more control over their time. (I’ve never been more excited to have a puppy!)
  • Thousands of hours commuting have been avoided, giving us back time in our days.
  • Many of our clients and customers have embraced remote strategies for their own teams. This has enabled our team to move more work remote while maintaining high customer retention rates.
  • We’ve found new ways to build culture, onboard employees, and solve problems — including organizing regular in-person offsite gatherings and paying for employee WeWork memberships.

Perhaps the biggest benefit is that we’ve been able to hire in any location nationwide (as well as in the UK), which accelerates our goals toward building a world-class, top-talent team. We don’t engage in micromanaging or hyper-surveilling employees, and we agree with these folks, who argue that async/remote-first is a major contributor to creating a maximally diverse team.

Datavant’s onsite interview

Our onsite is a series of tightly-packed interviews with several people around the organization.

The day starts with a conversation with one of the Datavant recruiters with whom you’ve been corresponding already. This person goes over the details they’ve sent in advance to make sure everything is clear, and to answer any questions you may have.

For my morning check-in, the recruiter’s dog kept making appearances in our chat, which, despite being conducted entirely over a screen, made it all feel more human and less formal.

(Less formal = more relaxed = clearer thinking = better conversations.)

Another bonus of remote work is that it’s easier to manage a pet while working from home. Since joining Datavant, I’ve also gotten my own dog, who occasionally makes appearances in meetings and our #catchat Slack channel.

Gustavo “Gus” Guzman (front). Sometimes we wonder why our internal pets Slack channel was named #catchat and not #dogtalk.

The technical portions: system design & algorithms

Two portions of the engineering onsite are technically oriented. Mine focused on system design and algorithms. Similarly to how I approached the technical interview I described in Part 1, I approached these interviews with the goal of communicating with my interviewer as much as possible. I tried to make my thought process as transparent as possible as I described how I was tackling solutions while biasing for speed and verbalizing tradeoffs. This communication not only helped my interviewers understand my thinking and problem solving, but also helped us connect on a human level.

The system design interview was similar to the debug in that it resembled the kind of work demanded by the job. I appreciated this as a candidate and appreciate it now while interviewing candidates. You can’t come into this session knowing all the background details of the situation, so a significant part of the system design interview is knowing the questions you would need to ask to be able to design the system.

The conversation started to resemble a conversation we might have while actually working together.

I was impressed at how articulately my interviewer could discuss how this work impacted Datavant’s business goals. The conversation started to resemble a conversation we might have while actually working together. We believe it’s important for engineers to stay close to product and business requirements in order to understand how best to scale systems, not just design things that scale in a one-size-fits-all generic manner. We want to hire people who are not only interested in building things, but interested in building things to accelerate the business as a whole.

Even if you don’t have full knowledge of specific technologies discussed, you can talk about how they work and why you’d use certain types of tools. A lot of my previous experience was working with proprietary software, which is not immediately transferable, but I figured out in the moment how to extrapolate. For example, you don’t need to know the inner workings of something like postgres to talk about relational databases.

The behavioral interviews

My focus on having a conversation with my interviewer carried me through the behavioral portion of the interview as well. I did not try to tell the interviewer what I thought they wanted to hear, but let myself be driven by my intuition for cultivating an interesting conversation.

It can be useful to have scenarios at top of mind that you’re ready to talk about, but entering this discussion with too rigid of a script for yourself ultimately doesn’t serve you or the interviewer. Overly-prepared answers can come across as stiff or disengaged. At the end of the day, if you are yourself, you’ll figure out whether the company aligns with your vision for your career. This sets us all up for success, as hiring is a two-way street.

Culture Champion

Throughout the day, I observed everyone I interacted with displaying the “Smart, Nice, Gets Things Done” traits Datavant espouses. I was delighted at how comfortable I felt with my interviewers — the stress of the interview actually seemed to diffuse rather than ramp up over the course of a long day.

Datavant calls its last interview a “Culture Champion” interview. My Culture Champion interview was with Aneesh Kulkarni, Datavant’s Head of Engineering. Aneesh was calm, open and inviting, but his approach was totally unique, and caught me somewhat off-guard. More than other interviewers, he was relentless in his lines of questioning (in a good way!) and it seemed to me that the thing he was really trying to assess was my resourcefulness. Given that I was trying to move away from a feeling of “learned hopelessness,” I found his approach immediately energizing. It was motivating to imagine how this would drive the culture of the engineering team as a whole.

The Offer

Datavant is in a state of hypergrowth, so we move as fast as possible when we’ve decided we would like somebody to join us. My job offer came the next day and included a time limit on acceptance. There was a lot to consider in that time: compensation, potential satisfaction, and career growth.

I was weighing Datavant’s offer against a counteroffer at a hedge fund, so I needed to move quickly to gain more information. I hadn’t dealt with equity offers before, so I set up a conversation with the Head of People to learn more.

Datavant has continued to be profitable.

I had also been slightly spooked by a friend who had a bad experience at a failed start-up, so I set up a conversation with an engineer to talk about the stability of the company. He walked me through the company’s financials, talked about how our merger with Ciox not only gave a significant degree of financial stability but also a very clear roadmap for future growth. He pointed out that post-merger, Datavant has continued to be profitable. This level of transparency was reassuring. Engineers at my other jobs certainly did not have monthly company financial data at their fingertips.

I set up 7 or 8 other calls trying to force difficult conversations about company culture, searching for a red flag or a weak link…but I never found one. Finally, after discussing opportunities for career growth with the person who would be my manager, I was convinced.

Here I should pause.

You’re reading this from outside of Datavant, perhaps steeped in a company culture where this article could come across as a little like corporate propaganda. Maybe the Datavant Talent Acquisition team knows that I’m eager to share my experience because it has been so positive, and they engaged me to write these pieces knowing they would be glowing brand-affirming testimonials.

I understand. I was just as skeptical, which is why I did my best at due diligence for myself after receiving the offer, calling everyone I could arrange a call with. But in describing my journey coming to Datavant, I’m not only trying to demystify our hiring process, I’m also trying to convey something of my experience of Datavant’s uniqueness. The word “unicorn” gets thrown around a lot in the startup world, and a healthy amount of skepticism has settled on a lot of people recently across tech. You should be skeptical of my enthusiasm.

But if your level of intrigue is even a tenth of a percent greater than your level of skepticism, then I would encourage you to check out our job openings and get on a call with a recruiter. See for yourself if our culture actually maps to how we talk about it.

I found a team and a vision of my future I didn’t know could exist.

Maybe you will too.

By Carlos Guzman, and Nicholas DeMaison.

Carlos Guzman started as an IC Engineer at Datavant in May 2022, became an Engineering Manager in July 2022, and is now the Engineering Chief of Staff. Read more about his journey, and connect with Carlos via LinkedIn.

Nicholas DeMaison writes for Datavant, where he leads talent branding initiatives. Connect with Nick via LinkedIn.

Does Carlos’ story sound interesting? We’re hiring remotely across teams.

Spotlight on AnalyticsIQ: Privacy Leadership in State De-Identification

AnalyticsIQ, a marketing data and analytics company, recently adopted Datavant’s state de-identification process to enhance the privacy of its SDOH datasets. By undergoing this privacy analysis prior to linking its data with other datasets, AnalyticsIQ has taken an extra step that could contribute to a more efficient Expert Determination (which is required when its data is linked with others in Datavant’s ecosystem).

AnalyticsIQ’s decision to adopt state de-identification standards underscores the importance of privacy in the data ecosystem. By addressing privacy challenges head-on, AnalyticsIQ and similar partners are poised to lead clinical research forward, providing datasets that are not only compliant with privacy requirements, but also ready for seamless integration into larger datasets.

"Stakeholders across the industry are seeking swift, secure access to high-quality, privacy-compliant SDOH data to drive efficiencies and improve patient outcomes,” says Christine Lee, head of health strategy and partnerships at AnalyticsIQ. 

“By collaborating with Datavant to proactively perform state de-identification and Expert Determination on our consumer dataset, we help minimize potentially time-consuming steps upfront and enable partners to leverage actionable insights when they need them most. This approach underscores our commitment to supporting healthcare innovation while upholding the highest standards of privacy and compliance."

Building Trust in Privacy-Preserving Data Ecosystems

As the regulatory landscape continues to evolve, Datavant’s state de-identification product offers an innovative tool for privacy officers and data custodians alike. By addressing both state-specific and HIPAA requirements, companies can stay ahead of regulatory demands and build trust across data partners and end-users. For life sciences organizations, this can lead to faster, more reliable access to the datasets they need to drive research and innovation while supporting high privacy standards.

As life sciences companies increasingly rely on SDOH data to drive insights, the need for privacy-preserving solutions grows. Data ecosystems like Datavant’s, which link real-world datasets while safeguarding privacy, are critical to driving innovation in healthcare. By integrating state de-identified SDOH data, life sciences can gain a more comprehensive view of patient populations, uncover social factors that impact health outcomes, and ultimately guide clinical research that improves health. 

The Power of SDOH Data with Providers and Payers to Close Gaps in Care

Both payers and providers are increasingly utilizing SDOH data to enhance care delivery and improve health equity. By incorporating SDOH data into their strategies, both groups aim to deliver more personalized care, address disparities, and better understand the social factors affecting patient outcomes.

Payers Deploy Targeted Care Using SDOH Data

Payers increasingly leverage SDOH data to meet health equity requirements and enhance care delivery:

  • Tailored Member Programs: Payers develop specialized initiatives like nutrition delivery services and transportation to and from medical appointments.
  • Identifying Care Gaps: SDOH data helps payers identify gaps in care for underserved communities, enabling strategic in-home assessments and interventions.
  • Future Risk Adjustment Models: The Centers for Medicare & Medicaid Services (CMS) plans to incorporate SDOH-related Z codes into risk adjustment models, recognizing the significance of SDOH data in assessing healthcare needs.

Payers’ consideration of SDOH underscores their commitment to improving health equity, delivering targeted care, and addressing disparities for vulnerable populations.

Example: CDPHP supports physical and mental wellbeing with non-medical assistance

Capital District Physicians’ Health Plan (CDPHP) incorporated SDOH, partnering with Papa, to combat loneliness and isolation in older adults, families, and other vulnerable populations. CDPHP aimed to address:

  • Social isolation
  • Loneliness
  • Transportation barriers
  • Gaps in care

By integrating SDOH data, CDPHP enhanced their services to deliver comprehensive care for its Medicare Advantage members.

Providers Optimize Value-Based Care Using SDOH Data

Value-based care organizations face challenges in fully understanding their patient panels. SDOH data significantly assists providers to address these challenges and improve patient care. Here are some examples of how:

  • Onboard Patients Into Care Programs: Providers use SDOH data to identify patients who require additional support and connect them with appropriate resources.
  • Stratify Patients by Risk: SDOH data combined with clinical information identifies high-risk patients, enabling targeted interventions and resource allocation.
  • Manage Transition of Care: SDOH data informs post-discharge plans, considering social factors to support smoother transitions and reduce readmissions.

By leveraging SDOH data, providers gain a more comprehensive understanding of their patient population, leading to more targeted and personalized care interventions.

While accessing SDOH data offers significant advantages, challenges can arise from:

  • Lack of Interoperability and Uniformity: Data exists in fragmented sources like electronic health records (EHRs), public health databases, social service systems, and proprietary databases. Integrating and securing data while ensuring data integrity and confidentiality can be complex, resource-intensive and risky.
  • Lag in Payer Claims Data: Payers can take weeks or months to release claims data. This delays informed decision-making, care improvement, analysis, and performance evaluation.
  • Incomplete Data Sets in Health Information Exchanges (HIEs): Not all healthcare providers or organizations participate in HIEs. This reduces the available data pool. Moreover, varying data sharing policies result in data gaps or inconsistencies.

To overcome these challenges, providers must have robust data integration strategies, standardization efforts, and access to health data ecosystems to ensure comprehensive and timely access to SDOH data.

SDOH data holds immense potential in transforming healthcare and addressing health disparities. 

With Datavant, healthcare organizations are securely accessing SDOH data, and further enhancing the efficiency of their datasets through state de-identification capabilities - empowering stakeholders across the industry to make data-driven decisions that drive care forward.

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