Data Engineering 📈

L1-L3 Learning to Lead Yourself

Level 1

Mastery (Base)

⚙ Technical knowledge:

‼️ Comfortable with using git and contributing to our code base

‼️ Can extend existing data models and design simple new ones

‼️ Creates new Looker views and dashboards; extracts basic insights quickly from existing Looker explores

‼️ Strong SQL skills

Implements basic prediction models quickly

Basic Python or R skills

Stats & Math:

Basic stats and math knowledge

Examples

Able to find a formula to calculate confidence intervals for different measurement scenarios, knows how to interpret those etc.

Business partnering:

‼️ Demonstrates a good understanding of the business team they are partnering with

Examples

Knows how the team is organised, who is who, what are the goals, current priorities, biggest challenges etc.

Analytics:

‼️ Translates business questions into analysable hypothesis and answers those

Examples

Question from business “Why do salaried users cost us twice as much on customer support?” → cost are allocated by number of intercom queries → salaried users must be generating more queries → Is of queries proportional to engagement? → Are all salaried users are over-proportionally struggling with particular problems (e.g. missing bank statements) → etc.

Picks the right visualisation types for the data at hand

Examples

distributions, time series, scatter plots etc

💪 Getting things done:

Delivers assigned tasks that meet expected criteria

Tries to unblock themselves first before seeking help

Works for the team, focuses on tasks that contribute to team goals

Influence

Improves documentation that is incorrect

Feedback

Provides regular status updates to their mentor/buddy

Points out syntactical improvements in code reviews

Writes PR descriptions that provide basic context for the change

Seeks guidance from other engineers, rather than answers

Level 2

Mastery (Base)

⚙ Technical knowledge:

‼️ Reasons well about about underlying principles of data modeling

Analytics:

‼️ Attention to details

Examples

whenever they deliver a piece of work or send a weekly KPIs report they don’t just blindly copy & paste; they sanity check whether things make sense and try to spot mistakes

💪 Getting things done:

‼️ Manages their own time effectively, prioritises their workload well, on time for meetings, aware when blocking others and unblocks

Examples

able to focus on assigned tasks despite distractions from people, emails, slacks etc. Able to create a “focus environment” for themselves

exhibits self-awareness around personal productivity (able to spot and debug personal productivity issues or to seek help/advice)

‼️ Brings things to completion

Examples

analysts/data scientists often exhibit a behaviour where they run many analyses in parallel for a prolonged time without closing tasks off. Closing a task off could mean writing down key takeaway and sharing the findings with the relevant audience.

brings a model into a production experiment instead of continuing to tweak offline results

Mastery (Data Analytics)

Mastery (Data Science)

⚙ Technical knowledge:

Familiar with ML batch serving techniques

Basic knowledge of standard ML approaches

Examples

linear regression, neural nets, clustering, random forests etc.

Influence

Proactively raises issues they spot in retrospectives

Feedback

Proactively communicates to their team what they are working on, why, how it's going and what help they need

Accepts feedback graciously

Gives feedback to peers when asked

Provides helpful and actionable feedback in code reviews in an empathetic manner

Take a look at the levelling up your code reviews talk for some ideas

Writes PR descriptions that provide context and provide rationale for significant decisions

“I decided to X instead of Y here, I also considered Z but for these reasons I went with X”

Level 3

Mastery (Base)

⚙ Technical knowledge:

‼️ Consistently applies data modeling best practices and suggests ways to improve current practices in non trivial cases

Analytics:

‼️ Able to pick the best tool and method to effectively help the business to answer a question/make a decision

Examples

Looker, SQL, python or spreadsheets + a basic chart, blackbox ML model or a structured scenario model etc) → Understands the problem at hand and proposes alternative suitable solutions rather trying to fit the problem to the favourite tool.

‼️ Able determine what really matters for a particular analysis and understands what a 80/20 solution would look like and can prioritise accordingly

Communication:

‼️ Concise, clear and effective communication

Examples

tailored to audience, clear and concise message (i.e no unnecessary details)

can be through emails, slack or presentations

Mastery (Data Analytics)

Business Partnering:

Asks why. Does not take truths for granted unless they understand exactly where they are coming from

Examples

especially wrt regulation, compliance, etc.

Mastery (Data Science)

Technical knowledge:

Able to pick the right ML method for the problem at hand; demonstrates good intuition of how those approaches work and what strength/weaknesses they have

Impact:

Distinguishes well between impactful ML problems vs just “predicting something”

Influence

Provides valuable input to RFCs from their team

Proactively improves modules, services, systems and codebases they encounter, 'this doesn't make sense, I'm going to do something about it'

Contributes to scaling engineering hiring (e.g. leads calls, does onsite interviews)

Builds simple tools or iterates existing tools for the benefit of all engineers

Feedback

Transparent about mistakes they've made, early

Proactively gives timely actionable feedback to peers

Proactively seeks feedback from the people around them

Considers the opinions of others before defending their own

L4-L6 Leading Others

Level 4

Mastery (Base)

💪 Getting things done:

‼️ Distinguishes clearly between urgent and important tasks and is able to focus on getting the important tasks done.

Examples

effectively manages expectations of other people

communicates priorities to their team and other relevant stakeholders

Holds themselves and others accountable

Accountability is about delivering on a commitment. It’s responsibility to an outcome, not just a set of tasks.

Leadership:

‼️ Actively drives improvements of how the team works

‼️ Values teams success over individual success and company’s success over teams success

Onboards / mentors new team members

Gets buy-in on technical decision-making and proposed designs

Sought out for code reviews

Communication:

Communicates complex ideas effectively

Examples

has the ability to chose the appropriate level of abstraction and make complexity easy to understand, tips

Mastery (Data Analytics)

Business Partnering:

‼️ Valued and trusted business partner for the teams they support

Examples

Can be mostly proxied by the type of questions their business partners are asking. “Can you help me to solve this (hard) problem?” vs “Can you please pull this number?”

‼️ Proactively identifies relevant/impactful areas for analyses which would deepen the understanding of the business or enable decisions

Examples

during the planning process you contribute proactively to help your team to define the right priorities with relevant insights

Mastery (Data Science)

⚙ Technical knowledge:

Thrown at fires and resolves / contributes heavily to resolving them

Replicates cutting edge approaches from research papers where required

Thinks about the future situations code will be used in, planning and acting accordingly

Debugs complex Deep Neural Net code/issues

Examples

knows what to look at when the loss is not decreasing etc.

💪 Getting things done:

‼️ Makes pragmatic choices about taking on tech debt

‼️ Validates ideas aggressively & iteratively

tackles the biggest unknowns first; validates ideas with 10% effort

Impact:

‼️ Measures, understands and is transparent about the impact of their ML work.

we should serve as role models for the rest of the company in this regard in particular

Influence

Positively influences engineers in the wider org

Maintains documentation on things they know the most, makes it easy for future engineers to interact with systems/code

Clears blockers for junior team members, provides context/guidance, or knows how to escalate

Asks why. Does not take truths for granted unless they understand exactly where they are coming from (especially wrt regulation, compliance, etc)

Drives changes to engineering practices with well-reasoned arguments and a "strong opinion, weakly held" mentality

Shapes the direction of systems designs with less experienced engineers

Breaks down delivery and knowledge silos in their squad

Feedback

Proactively gives feedback "upwards" and to people they interact with who are not in their team

Transparent in making design and technical decisions

Helps people in non-technical roles understand technical constraints / trade-offs

Shares technical context and direction for less experienced engineers

Gives direct and constructive feedback to other engineers

Level 5

Mastery (Base)

💪 Getting things done:

‼️ Builds out a strong internal network

i.e. well connected through-out the company, also to teams with no direct common projects at the moment

‼️ Solves larger ambiguous/not well defined problems

Has good organisational awareness

Examples

understands the process of how things are getting done in the company e.g. how and when goals are set, how decisions are being made, how priorities are defined etc.

Impact:

Sees common patterns in similar tasks and thinks about the solution from the platform/systems perspective.

Examples

solutions that not only solve your own problem but also similar problems of other people in the company)

Leadership:

‼️ Contributes to maintaining Monzo’s culture in the wider company

‼️ Proactively thinks about how we can get better at our purpose: quicker and better decisions based on data

Mastery (Data Analytics)

Domain Knowledge:

Deep domain knowledge in specific areas, can go lower than almost anyone else

Examples

deep credit risk knowledge, user behaviour analytics etc

Mastery (Data Science)

⚙ Technical knowledge:

Familiar with ML streaming, stateful and stateless serving techniques

Examples

can spec out and plan an implementation. Familiar with technological components that might be required

Technical authority within their immediate peer group (team/platform), the natural escalation point

Influence

Represents Monzo at conferences/events

Given as reason for other engineers to join Monzo

Proactively shares knowledge internally

Acts as the 'sole proprietor', in the CEO mindset, their ego/agenda is not a factor in their thinking or decision making

Feedback

Helps other people develop themselves and regularly gives insightful, useful feedback to those around them.

Talks to non-technical stakeholders on appropriate level of abstraction

Level 6

Mastery (Base)

Leadership:

Delivers projects that require cross functional collaboration

Delegates to make better use of their time

Mastery (Data Analytics)

Business Partnering:

Comfortably supports and interacts with C-level executives

Mastery (Data Science)

⚙ Technical knowledge:

Serves as a technical authority in the wider data science community

Deep domain knowledge, can go lower than almost anyone else

Makes targeted improvements in stability, performance and scalability across our platform

Impact:

Measurable impact on company level goals

Influence

Attracts other very senior hires

Engineers around them get better and have a bigger impact, faster

Feedback

Transparent about feedback they have received and what they are going to do differently

L7-L8 Leading Organisation