Senior Data Engineer – Growth Tracker

[ Name ] Senior Data Engineer – Growth Tracker

SDE  ·  SFIA 4-5  ·  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
Actively monitors developments in data engineering - emerging architecture patterns, new tooling, industry case studies - and evaluates their relevance to the organisation's context.
Invests in depth across the data stack - not just pipeline development but storage optimisation, compute patterns, and data governance frameworks.
Seeks out peer review and challenge from other senior technical practitioners, not just validation.
Identifies areas where the team's collective capability needs to grow and creates structured opportunities to develop it.
Brings external perspectives into the team - from conference talks, industry publications, and peer network conversations.
Reflects critically on past architectural decisions - capturing what was learned and ensuring those lessons shape future choices.
Delivery
Leads the delivery of large, complex data platform work - coordinating across engineers, managing dependencies, and ensuring coherent technical execution.
Maintains personal delivery velocity on significant technical work alongside leadership and mentoring responsibilities.
Drives delivery rhythm on platform improvement programmes - breaking ambiguous work into deliverable increments and maintaining momentum.
Identifies and removes delivery blockers for the broader team - dependency management, decision facilitation, technical unblocking.
Manages the tension between long-term platform investment and short-term delivery pressure - making reasoned trade-offs transparently.
Ensures large deliveries land fully - monitoring, documentation, knowledge transfer, and team readiness - not just code in production.
Quality & Craft
Sets the quality standard for the discipline - their own work is a reference implementation of what good data engineering looks like.
Leads the design of data quality frameworks that provide genuine confidence in data reliability across the platform.
Identifies systemic quality problems - fragile ingestion patterns, untested transformation logic, missing data contracts - and drives structured remediation.
Reviews architectural proposals critically - identifying failure modes, scalability limits, and operational risks before they are built in.
Champions data modelling rigour - ensuring the organisation's data models are designed for long-term usefulness, not just immediate delivery.
Establishes engineering standards that others apply consistently - pipeline patterns, testing requirements, documentation standards.
Communication
Communicates architectural decisions with clarity and rigour - presenting options, trade-offs, and recommendations in formats accessible to both technical and non-technical audiences.
Writes architecture decision records (ADRs) and technical documentation that stand as durable records of intent and reasoning.
Influences without authority - building persuasive cases for technical direction through evidence, credibility, and relationship.
Facilitates technical discussions productively - drawing out perspectives, resolving disagreement, and reaching clear decisions.
Communicates data engineering risk clearly to senior leadership - translating technical concerns into business impact language.
Represents the data engineering discipline in cross-functional forums with confidence and depth.
Collaboration
Builds strong, trust-based relationships with senior stakeholders - data product owners, heads of analytics, platform leads - enabling effective cross-boundary collaboration.
Works with the Data Architect to align team delivery with long-term technical strategy.
Partners with platform engineers to ensure the data platform is built on reliable, maintainable infrastructure.
Drives collaboration across data domain teams - establishing shared standards, shared tooling, and mutual accountability for data quality.
Creates a collaborative culture within the data engineering team - psychological safety, knowledge sharing, and open technical debate.
Represents data engineering interests in product and delivery planning, ensuring data work is appropriately sequenced and resourced.
Ownership
Takes accountability for the health and direction of the data platform - not just their own work but the coherence and reliability of the whole.
Leads incident response for significant data platform failures - investigating, communicating, resolving, and preventing recurrence.
Owns the quality of the team's output collectively - raising concerns about standards, practices, and technical direction when needed.
Makes bold technical recommendations when the evidence supports them - not defaulting to safe, familiar choices when better options exist.
Takes responsibility for the technical environment junior and intermediate engineers work in - setting them up for success through standards, tooling, and support.
Holds themselves and others accountable for the long-term health of the data platform, not just short-term delivery.
Technical Foundation
Demonstrates mastery-level SQL and Python capability and applies it in the design of high-quality, production-grade platform components.
Designs and validates data architectures at scale - understanding the failure modes, operational costs, and evolution paths of significant architectural choices.
Operates at the intersection of data engineering and data platform engineering - designing systems that are observable, reliable, and maintainable at scale.
Deep understanding of cloud cost optimisation, query performance at scale, and storage economics in cloud data platforms.
Maintains current knowledge of the data tooling landscape - understanding the capabilities and trade-offs of leading tools in orchestration, transformation, cataloguing, and quality.
Understands data governance - lineage, cataloguing, access controls, and regulatory considerations - and incorporates governance requirements into architectural decisions.
Contributes to the data engineering discipline beyond the team - writing, speaking, or open source contribution that builds the organisation's external reputation.
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  ·  Senior Data Engineer Growth Tracker