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The Anatomy of a High-Performing Engineer

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Datavant
August 5, 2021
 min

The Datavant Way to measure and cultivate performance

We’ve all seen the silly jargon: rockstar, ninja, superstar, 10x’er. They all mean one thing: a very strong performing software engineer. We want to work with them; we want to be them. But how do you know it when you see it? In this piece, I’ll walk through the framework we use to assess and cultivate performance on the engineering team at Datavant.

We Grow Leaders

“We grow leaders” is one of our core values — this includes both individual contributors who lead initiatives and managers who lead teams. Career growth is generally defined as an increase in impact, skillfulness, and independence carrying out larger and larger scopes.

We use a simple rubric with 3 categories:

Accelerates the team

  1. Recruitment
  2. Team-first orientation
  3. Feedback

Gets things done

  1. Autonomy
  2. Communication
  3. Bias for speed

Demonstrates excellence in engineering

  1. Testing, tools, and infrastructure skills
  2. Programming skills

Each quarter, every engineer at Datavant is scored as “Skillful,” “More Skillful,” or “Super Skillful,” across each of the 8 metrics.

A representative engineer might have this profile:

Why we do this

We do this to provide clear expectations, actionable feedback, and answer the question that fuels many growth-oriented people: “So, how do I progress?” Clear expectations on how to be a high performer also mitigate bias because expectations are known, communicated, and universally applied.

Skillful corresponds to our baseline expectations for an engineer; a good way to think about this is that a new grad engineer at Datavant should be at least Skillful in every category.

For experienced engineers, we believe in playing to our strengths, which means we embrace people who are “well-lopsided.” As an engineer grows, we expect them to become Super Skillful in at least 1 category. Engineers can progress either by going beyond Super Skillful in 1–2 metrics, or becoming More Skillful across multiple areas. Both types of engineers are valuable and celebrated at Datavant.

What it looks like in practice

Accelerate the Team — what a “force multiplier” looks like

Success at Datavant means more than simply producing a lot of output in isolation. It means making your teammates more effective, supporting them when they need help, holding them to the same high expectations you hold yourself, and questioning processes that slow the team down. There are 3 areas we focus on:

Recruitment

Recruiting is a core aspect of every engineer’s job. Datavant is always hiring for exceptional engineers, and the team is responsible for both assessing and selling candidates.

  • Skillful: conduct interviews and refer candidates
  • More Skillful: write interview questions, train interviewers, and write blog posts
  • Super Skillful: shape our recruitment process, making successful changes to how we hire talent

Team-First Orientation

Everything our engineering team has accomplished has been built upon the bedrock of a strong team-oriented culture. We’re a team of collaborators.

  • Skillful: approach conflict with an open mind (not defensively), operate with a “no job too big/no job too small” attitude, and collaborate effectively with other engineers.
  • More Skillful: encourage and invite quieter teammates to share their opinions, create a space where everyone feels ownership and can contribute.
  • Super Skillful: mentor other engineers, successfully helping them level up their skills.

Feedback

“Feedback is a gift” is one of our values at Datavant, but it’s not just something you get from your manager. Everyone is responsible for giving and receiving direct, actionable feedback.

  • Skillful: give meaningful feedback when requested and receive feedback with grace
  • More Skillful: proactively deliver and seek out feedback
  • Super Skillful: improve the ways our team provides feedback.

Get Things Done — what results oriented looks like

Datavant is a hyper growth company, and our engineers create value by getting things done. We believe in hustle as a strategy. Datavant engineers deliver value when they write high quality production code and ship new features and products to customers. In measuring this, we focus on 3 areas:

Autonomy

Autonomy is a measure of how big and hairy a problem we can give an engineer and have them find a way to solve it. Asking questions is a key part of this — most autonomous engineers are exceptional at identifying exactly which questions they need to ask to solve a problem. We hire talented, smart engineers who we can trust to self manage, that means our expectation is that you will ask for help when you need it and not stay stuck.

  • Skillful: estimate tasks and work independently for a day or so at a time
  • More Skillful: self-manage and work independently for a week
  • Super Skill: identify problems and work independently more or less indefinitely when necessary

Communication

This can be a blurry word; what it means to us is:

Do they push information to stakeholders proactively?

Do they ask questions and pull information from experts when required?

  • Skillful: share information effectively with their smaller, immediate team and sometimes ask for help when needed, but sometimes end up stuck and waiting for help.
  • More Skillful: proactively share information with their broader team, including negative news, risks, and delays, and always ask for help when needed.
  • Super Skillful: proactively build an understanding of people’s needs from across the team and establish alignment across the entire company, ultimately increasing the effectiveness of the engineering team.

Bias for Speed

“Perfect is Good, Done is Better,” is a Datavant value, and it means we bias toward speed. The behaviors in this metric are all about prioritization and volume. There’s always more to be done than we can expect to do, so prioritizing the highest value work is essential, and the expectation is that more experienced and skillful engineers will get more total volume of work done.

  • Skillful: seek input on prioritization and balance work within a single project
  • More Skillful: balance work across multiple projects and build alignment on prioritization
  • Super Skillful: help the engineering, product, and design teams (the “tech org”) prioritize better; find ways to deliver success, even in the face of changing circumstances

Excellence in engineering — what the fundamentals look like

Excellence in engineering is what most teams typically think of engineering skills. While all the skills on our rubric are important, and we firmly believe in playing to strengths and not having a single engineering profile, it’s impossible to advance as an individual contributor without establishing your excellence in engineering.

Testing, Tools, and Infrastructure Skills

Source code alone isn’t very valuable. Testing, tools, and infrastructure skills are all about everything else that’s required to have a successful code base.

  • Skillful: write high quality tests and generally understand production infrastructure
  • More Skillful: debug, modify, and build production infrastructure
  • Super Skillful: proactively plan and implement improvements to our infrastructure that improve the quality of our services across teams

Programming Skills

Programming skills are the most fundamental engineering competency.

  • Skillful: write clean, correct, modular code and ask for advice when needed
  • More Skillful: independently break down multi-week problems into manageable PRs and design and deliver high quality code
  • Super Skillful: independently break down multi-month projects into components; write engineering plans that stretch out beyond a year; lead and mentor other engineers to design and implement these components.

Summary

Measuring performance is highly specific to the organization’s mission, culture, and structure. There is no one size fits all rubric, and a good rubric will grow and adapt with the organization. What is important is that your team knows exactly what it means to exceed expectations. I believe people want to perform at their best. If you paint a clear picture of what it means to perform and provide direct, actionable feedback, your team will exceed expectations.

Join us

If you’re reading the above post and thinking, “I wish I had clear expectations like this,” or “getting feedback that way sounds incredible!” then join us! We’re connecting the world’s health data to improve patient outcomes, and we’re hiring!

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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