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Unlocking the Datavant Engineering Interview Process: Part 3

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Datavant
June 3, 2023

In the first two articles in this series, I discussed managing interview processes and unpacked my experience with Datavant’s virtual onsite interview. Here, I’m going to put a microscope on my first week at Datavant and discuss a bit of my Day 1 Project.

Want to join us? We’re hiring.

But first, “Day 0”

New Datavanters officially start on Tuesdays. I knew about Datavant’s “Day 1 Projects” from early conversations with my new manager, so I expected my first day to be an explosion of “Day 1 intensity.” Even though we want new Datavanters to hit the ground running, your actual first Tuesday isn’t really “Day 1,” it’s more like a “Day 0” — the day to get oriented, get access everywhere you need it, set up your platforms, put up internal profiles, and have a 1:1 with your manager. Obviously you can’t start working on a project without access, but this easing-in nevertheless caught me somewhat off-guard. I kept wondering, “But where’s all the work??” This designed “admin day” is Datavant’s way of setting folks up for success on “the work” as quickly as possible by avoiding as much access-stumbling as possible once into a project.

Datavant is intensely committed to internal transparency. “Access” doesn’t just mean access to your specific corner of Github and your specific team’s working drive. Salaried employees with shared drive access can comb through financial reports, org charts, vision docs, Board presentations, product demos, and retrospectives diagnosing issues and responses. And because we are an async-first culture, it’s also possible, in many cases, to follow archived discussions through the comment threads within these documents:

When you join a new company, it can be difficult to understand how macro priorities relate to your personal mandate. The “process archive” held in doc comment threads is incredibly valuable for orienting to different perspectives, layers of priorities, and what many different teams are working on. Once I had access everywhere I needed it, I took delight during my first week in simply exploring (and having the time to explore) this vast archive.

Moving into a health tech company from a fin tech company, I also had to get up to speed on healthcare-specific jargon. Who is a “payor”? What’s an “HIS”? “FHIR”? I did some preliminary research, but the healthcare industry is incredibly vast and the jargon expansive. My shared drive exploration also helped pinpoint key jargon areas to focus on.

Onboarding Buddies and personal connection

Lots of people wonder how easy it is to create personal connections in a remote-first workplace. Onboarding buddies are more than just a point person to help you learn your new job. Many people around the Engineering group took time to help me ramp up quickly on things like our precision and recall metrics in Match, the areas our data scientists focus on, and issues in our security posture. Ideally, the Onboarding Buddy (who is often from a different team) goes a step further, making themselves available for all of the “other” questions (e.g. “What does personal growth really mean at Datavant?”), and passing on local knowledge (highlighting relevant Slack channels and mailing lists). In my case, my onboarding buddy had managed one of our core products (Switchboard) and in a very Datavant-y twist after deciding to become an individual contributor again, actually moved onto my team when I became a manager.

Co-working Days and offsites are another major opportunity for personal connection. I can only speak from my experience living in New York City, but I can say that New York-based Datavanters organize Co-working Days as often as once a week. The Engineering team holds several offsites each year, alternating gatherings of the full team with gatherings of pods or sub-teams. Therefore, it is highly likely that new hires will have an offsite within the first three or four months of being hired. My first offsite happened to fall two weeks into my tenure at Datavant, which was amazing timing for hitting the ground running with personal connections.

#RealDay1

Part of Datavant’s unique culture is our “more responsibility, fewer rules” approach. To instill this mindset right from the outset, Datavanters are given significant Day 1 Projects as soon as they join. You can find snapshots of Day 1 Projects peppered around Datavant’s online world: Anjali built out a “Configurator;” Aleah and Isa helped stand up the COVID-19 Research Database; Nick put together the Tech Blog you’re reading now.

My first project was to set up a model inference Python service in lieu of the in-process Java implementations that historically demanded manual translation from the Python code the modelers (data scientists) wrote to Java. This project decoupled the models from Java so that no translation would be needed, which had the added benefit of being able to update the models dynamically and seamlessly.

After the initial design and some work on it, I became a manager for the De-identified Match Pod so there was less time to code directly on the project, but I wanted to offer opportunities for the team to get more involved in it for their own growth. The team was still working on the project by the time I moved on to my next stage at Datavant, so “my first project” became my first team’s project.

NHOs

I’ve been at companies where you spend your first couple weeks entirely in new hire orientations. Datavant’s new hire orientations are structured so as to give you the time you need to get into your Day 1 Project. They unfold gradually over the course of the first month, and range in topics from HIPAA Certification (which I knew about but couldn’t appreciate fully until I was actually working with our data) to overviews of the company vision, core products, and the ecosystem in which we operate. The fact that I was actively working on my team while doing the NHOs made it much easier to connect the dots on what I was learning, as I could directly apply what I learned in the NHOs to my work and vision for the team’s product.

Searching for weak links

In my discussion of the Datavant Onsite Interview, I mentioned looking for red flags and weak links — any sign that the actual culture inside the company did not live up to the way it was being described. I continued my probing even after officially joining. I requested meetings with every leader whose name came up repeatedly in conversations. Everybody met with me and took my questions seriously. I dropped a ridiculously lengthy question about the philosophical and business implications of our “neutral & ubiquitous” stance in the CEO’s #AskMeAnything Slack channel. He responded with an impressively articulate response about how he understands both “neutral” and “ubiquitous” and how he sees these ideas playing out in day-to-day operations.

Travis has since shifted from his position as CEO to Board President, but Pete McCabe continues the practice of an #AskMeAnything channel, offering both weekly roundups and responses to Datavanter questions:

“If you are growing fast, you have a different company every 6-12 months.”

Elad Gil made this observation about Google and Twitter, but it is equally true for Datavant. Almost 300 salaried employees have joined Datavant since I started less than a year ago. The merged entities of Datavant and Ciox have taken several major steps toward one unified org structure, meaning that the full Datavant workforce is close to 10,000 people. We’ve had several reorganizations as a result. Engineers have become managers. Managers have returned to IC roles. And we continue to look for the best structures to offer the best opportunities for product development. People are still eager to cultivate personal connection, but that feeling of “I know who everybody is and what they do” is admittedly less accessible than when the company was smaller. This is an invariable tradeoff as a company grows, but what continues to impress me is the broad commitment to the “smart, nice, get things done” culture that I observe even as we scale.

Still true

Almost a year into my Datavant journey, I stand by the assertions I made in my very first Datavant Tech Blog post: the intentionality behind this culture continues to amaze me, and I continue to want to build this culture. As Datavant transitions into the next version of itself, I couldn’t be more excited to help drive our future. Does this sound interesting? Check out our open roles and be in touch.

By Carlos Guzman and Nicholas DeMaison with input from Alicia Loewenthal.

About the authors

Carlos Guzman started as an IC Engineer at Datavant in May 2022, became an Engineering Manager in July 2022, had a stint as Engineering Chief of Staff and is now Engineering Head of Identified Switchboard. 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.

We’re hiring remotely across teams. Check out our open positions.

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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