Graduate Data Engineer – Growth Tracker

[ Name ] Graduate Data Engineer – Growth Tracker

GDE  ·  SFIA 1-2  ·  raganmcgill.co.uk

1Novice
No evidence of this yet · Lacks experience in this competency · Requires significant training and guidance
2Developing
Evidence of trying but lacking consistency · Demonstrates effort and initial attempts · Progressing, consistency is needed
3Proficient
Evidence of doing this with areas for improvement · Competent with some areas for enhancement · Meets most expectations
4Accomplished
Evidence of consistently meeting expectations · Highly reliable in delivering results · Maintains performance standards
5Expert
Evidence of exceeding expectations · Demonstrates exceptional mastery · Autonomous · Leads and mentors others
Learning & Growth
Delivery
Quality & Craft
Communication
Collaboration
Ownership
Technical Foundation
Learning & Growth
Approaches every data task as an opportunity to learn, not just to complete.
Asks questions without hesitation and seeks to understand the "why" behind data modelling and pipeline decisions.
Applies feedback consistently and tracks personal development over time.
Reads widely - documentation, dbt guides, data engineering blogs - to build context and understanding of the discipline.
Reflects regularly on their own progress, identifying gaps and discussing them with their TTL or mentor.
Shows willingness to learn from data mistakes - a bad query, a miscounted aggregate - without defensiveness.
Seeks out pairing opportunities proactively rather than waiting to be invited.
Delivery
Completes assigned data tasks reliably within agreed timeframes with close guidance.
Raises blockers early rather than pushing through silently.
Takes quality seriously from the start, even on small pieces of pipeline work.
Follows the agreed development workflow - branching, committing, opening PRs - consistently and correctly.
Responds to review feedback promptly and addresses it thoroughly before requesting re-review.
Keeps task status up to date in the team's tracking tools so the team has an accurate picture of progress.
Makes incremental, reviewable commits with clear messages that describe what changed and why.
Quality & Craft
Writes SQL and Python that is readable and follows the team's style conventions with support from a senior engineer.
Begins writing basic data quality assertions or tests for their own changes, guided by a more experienced colleague.
Reads and understands test coverage for the pipeline areas they are working in, asking questions about gaps.
Follows the team's definition of done and checks their own work against it before requesting review.
Avoids submitting pipeline changes with known data issues or unresolved questions without prior discussion.
Develops an awareness of common data quality problems - nulls, duplicates, schema drift - and flags them when encountered.
Learns what good code review feedback on data transformations looks like by observing and receiving it consistently.
Communication
Provides clear, concise updates in stand-ups - what they worked on, what they plan to do, what is blocking them.
Writes PR descriptions and documentation that give reviewers enough context to understand the data changes.
Asks questions in writing when appropriate so that the answer can benefit the wider team.
Communicates learning needs honestly with their TTL and mentor.
Responds to messages and review comments promptly during working hours.
Summarises their understanding when given verbal instructions to confirm correct interpretation.
Escalates concerns about data quality or timelines to their TTL early rather than hoping the problem resolves itself.
Collaboration
Contributes positively to team energy and culture.
Communicates openly and asks for help when needed.
Respects the expertise of more experienced colleagues while building their own voice.
Participates actively in stand-ups, retrospectives, planning sessions, and team discussions.
Pairs with senior engineers willingly and engages during sessions rather than passively observing.
Offers help to teammates when capacity allows, even in small ways such as reviewing a query or sharing something recently learned.
Respects agreed team norms around working hours, communication channels, and collaboration tools.
Ownership
Takes responsibility for completing tasks they have committed to, rather than waiting to be chased.
Follows through on review actions and does not consider a task done until it has met all agreed criteria.
Flags uncertainty about a data requirement or approach rather than making an assumption that leads to rework.
Keeps their own task board updated so the team always has an accurate picture of progress.
Owns their learning plan and does not wait for opportunities to be handed to them.
Acknowledges mistakes openly, explains what happened, and focuses on what they will do differently next time.
Takes the initiative to read relevant data documentation before asking a question that is already answered.
Technical Foundation
Develops working SQL proficiency and applies it in delivered data tasks under guidance.
Uses Git competently for branching, committing, and raising pull requests as part of everyday work.
Reads and navigates existing pipeline code to understand context before making changes.
Begins to understand the team's testing and data validation approach and why data quality matters.
Learns the team's deployment and orchestration process at a conceptual level.
Builds familiarity with the team's development environment, tooling, and cloud data warehouse.
Understands the basic data architecture of the system they are working in well enough to make safe, localised changes.
Evidence & examples
Evidence & examples
Evidence & examples
Evidence & examples
Evidence & examples
Evidence & examples
Evidence & examples

Strengths to recognise

Development focus areas

Overall assessment & agreed actions

raganmcgill.co.uk  ·  Graduate Data Engineer Growth Tracker