Performance Calibration Tool
Intermediate Data Engineer · Performance Levels
Fill in the fields below, then save as PDF or print at A2 landscape.
⎘ Copy to Clipboard
⎙ Print / Save as PDF
Role
Intermediate Data Engineer
Name
Manager
Period
Date
Level 1
Unsatisfactory
Low
Individual
Impact
Fails to design and deliver pipeline solutions independently despite having a well-understood problem domain.
Data quality frameworks in their area are absent or provide only cosmetic coverage with no real failure mode protection.
Does not provide meaningful mentoring to junior engineers; reviews are superficial or withheld entirely.
Technical decisions are made without documented reasoning, making review and course-correction difficult.
Examples
Delivered a pipeline after three sprints that lacked any orchestration error handling or data freshness monitoring.
Junior engineer's PRs were rubber-stamped without data quality or modelling feedback over an entire quarter.
Dampeners
Was placed in an ambiguous domain with no prior pipeline work to reference or extend.
Significant personal circumstances affecting focus and engagement during this period.
Progression Signal
Delivers one complete pipeline solution with meaningful data quality tests, monitoring, and documentation.
Provides one structured, substantive review to a junior engineer's data work.
Business Impact
Impact
Data consumers experience reliability issues from pipelines that lack proper quality controls and observability.
Junior engineers are not developing, reducing the team's capacity to take on delivery at pace.
Examples
A production data incident caused by an unmonitored pipeline required two days of senior engineer triage.
Dampeners
Some of the delivery failures may reflect unclear scope or missing product context for the domain.
Progression Signal
Pipeline incidents in their domain reduce and junior engineers begin showing measurable growth.
Mid
Individual
Impact
Delivers pipeline work but with significant quality gaps - missing explain plan analysis, fragile orchestration patterns, no data contracts.
Does not refactor within delivery; accumulates pipeline technical debt without flagging it to the TTL.
Contributions to technical discussions are passive; does not advocate for data quality or modelling standards.
Examples
Delivered a high-volume pipeline without any query performance analysis; it caused warehouse cost spikes in production.
Attended data architecture discussions but made no substantive contribution over a month of meetings.
Dampeners
May lack confidence to advocate in technical forums; needs explicit encouragement and a safe space to contribute.
Progression Signal
Begins flagging technical debt proactively rather than waiting to be asked.
Makes at least one substantive contribution to a technical design discussion.
Business Impact
Impact
Unmeasured pipeline performance and unmanaged technical debt create operational cost and reliability risks.
Passive technical engagement means missed opportunities to prevent avoidable platform problems.
Examples
Cloud warehouse costs increased 20% in a sprint due to an unoptimised query pattern shipped without review.
Dampeners
Business impact is still bounded by the scope of intermediate pipeline work.
Progression Signal
Performance issues and cost spikes attributable to this engineer's pipelines reduce.
High
Individual
Impact
Persistent delivery failures and resistance to technical feedback create a pattern that is difficult to attribute to circumstance.
Data quality frameworks in their domain are deteriorating, not improving, under their ownership.
Junior engineers assigned to them for mentoring are not developing; the mentoring relationship is ineffective.
Examples
Same orchestration anti-pattern appeared in three consecutive pipeline PRs after specific review feedback.
Junior engineer reported no useful guidance received from this individual over a full quarter.
Dampeners
Formal support structure and documented development plan should be in place before escalating further.
Progression Signal
Shows genuine responsiveness to structured feedback; at least one measurable change in technical behaviour.
Business Impact
Impact
Sustained data quality degradation in their domain is eroding analyst confidence in the data platform.
Junior engineers not developing means team capacity is not growing as expected.
Examples
Analytics team raised three data quality concerns in one month, all traceable to this engineer's domain.
Dampeners
Business impact, while significant, is domain-scoped; broader platform not yet affected.
Progression Signal
Data quality incidents in their domain reduce; junior engineers in their orbit begin to show progress.
Level 2
Development Needed
Low
Individual
Impact
Designs pipeline solutions but with gaps - missing edge case handling, insufficient data quality coverage, or weak documentation.
Mentoring provided to junior engineers is reactive rather than structured; feedback is inconsistent in quality.
Technical debt is identified but not escalated; problems accumulate in the pipeline rather than being surfaced.
Examples
Designed an ELT pipeline that handled the happy path well but lacked handling for source schema changes.
Provided junior engineer with useful ad-hoc feedback but no structured guidance on their development priorities.
Dampeners
Complexity of the data domain may be stretching current capability; additional context and support warranted.
Progression Signal
Begins incorporating edge case handling and schema evolution considerations into pipeline designs proactively.
Establishes a regular, structured pairing cadence with at least one junior engineer.
Business Impact
Impact
Pipeline fragility creates maintenance overhead and periodic data quality issues for downstream consumers.
Junior engineers not receiving structured mentoring are developing more slowly than the team's growth plan requires.
Examples
A source schema change broke a pipeline that had no schema evolution handling, causing a two-day data gap.
Dampeners
The team's mentoring and documentation standards may not have been communicated clearly enough.
Progression Signal
Pipeline fragility incidents reduce; junior engineers mentored by this individual begin to show faster growth.
Mid
Individual
Impact
Delivers pipeline work independently but does not yet drive data quality improvements proactively within their domain.
Technical decisions are sound but not well-documented; rationale is verbal and lost after conversations.
Data model designs follow existing patterns without questioning whether they are the right fit for new requirements.
Examples
Built a high-performing pipeline but without an architecture decision record or design rationale note.
Applied a star schema to a domain that would have been better served by a one big table approach, without evaluating alternatives.
Dampeners
Documentation and design rigour expectations may not have been clearly set for this engineer.
Progression Signal
Begins documenting design rationale and trade-offs on pipeline decisions as a default practice.
Evaluates more than one data modelling approach before selecting, and articulates the reasoning.
Business Impact
Impact
Undocumented pipeline decisions create knowledge dependency and slow the onboarding of engineers into the domain.
Suboptimal data model choices create rework costs for analytics teams downstream.
Examples
A new engineer joining the domain required three weeks to understand pipeline context that should have been documented.
Dampeners
Business impact is limited; the pipelines work, the knowledge gaps are the primary concern.
Progression Signal
Domain documentation quality improves to the point where others can navigate and extend pipelines independently.
High
Individual
Impact
Delivers pipeline work but avoids the harder problems - performance optimisation, data quality framework design, complex orchestration.
Feedback from senior engineers about architectural trade-offs is accepted but not internalised into changed practice.
Mentoring contribution is minimal; junior engineers are not being actively developed.
Examples
Consistently picks well-understood pipeline tasks and defers complex platform work to the senior engineer.
Same data modelling approach applied to three domains without considering whether it was the best fit.
Dampeners
May lack confidence in their own technical judgement; targeted stretch assignments with safe-to-fail framing may help.
Progression Signal
Accepts a technically challenging pipeline task and engages with it substantively rather than deferring.
Business Impact
Impact
Technical growth below expectation for tenure means the team continues to rely disproportionately on senior engineers.
Junior engineers not being developed means team capacity and capability are not growing as the business requires.
Examples
Senior engineer still leading all data architecture discussions eighteen months into this engineer's tenure.
Dampeners
Risk is in medium-term team scaling, not immediate delivery failure.
Progression Signal
Leads one moderately complex technical design independently and documents the rationale clearly.
Level 3
Consistently Delivers
Low
Individual
Impact
Designs and delivers complete ELT pipelines independently - from ingestion through transformation to serving - within their domain.
Implements meaningful data quality frameworks including dbt tests, freshness assertions, and anomaly monitoring.
Documents pipeline designs, data model rationale, and trade-offs clearly so others can understand and maintain them.
Examples
Designed and delivered a complete ingestion-to-mart pipeline for a new data domain with full quality test coverage.
Wrote a clear ADR for a data modelling decision that described the trade-offs and the reasoning behind the chosen approach.
Dampeners
Working within a well-understood domain; not yet tested on highly ambiguous or cross-cutting pipeline problems.
Progression Signal
Begins leading pipeline design conversations rather than just implementing designs agreed with the senior engineer.
Business Impact
Impact
Reliable, independently delivered pipeline work with appropriate quality controls reduces consumer-facing data incidents.
Clear pipeline documentation reduces onboarding friction and knowledge dependency on individual engineers.
Examples
A new junior engineer was able to extend a pipeline they owned within a week using the documentation provided.
Dampeners
Business impact is primarily within their assigned data domain; cross-domain influence not yet established.
Progression Signal
Contribution to data platform reliability begins to be recognised by analytics stakeholders in their domain.
Mid
Individual
Impact
Delivers independently across the full pipeline lifecycle and is beginning to identify and drive platform quality improvements.
Provides structured, effective mentoring to junior engineers - pairing regularly, giving targeted feedback, tracking growth.
Contributes substantive, well-reasoned opinions to data architecture and design discussions.
Examples
Led a data quality improvement programme in their domain that reduced consumer-reported issues by half.
Pairing sessions with a junior engineer produced measurable improvement in their SQL and dbt model quality.
Dampeners
Still primarily domain-focused; cross-team technical influence is developing but not yet consistent.
Progression Signal
Is invited to contribute to technical discussions outside their immediate domain, signalling growing credibility.
Business Impact
Impact
Data quality improvements they drive reduce the rate of analyst complaints and investigation requests.
Effective junior mentoring grows team capacity, reducing the dependency on senior engineers for pipeline delivery.
Examples
Analyst team reported a measurable reduction in data investigation requests following quality improvements in this domain.
Dampeners
Business impact is still primarily domain-scoped; not yet driving platform-wide quality improvements.
Progression Signal
Data quality and capacity contributions begin to benefit teams beyond their immediate domain.
High
Individual
Impact
Delivers complex pipeline work independently and is beginning to drive quality and practice improvements across domain boundaries.
Mentoring is effective and consistent - junior engineers in their orbit are developing at a noticeably faster rate.
Technical contributions in design discussions are respected and begin to shape team-wide pipeline practices.
Examples
Proposed a data quality monitoring pattern that was adopted across the team's entire pipeline estate.
Junior engineers they mentored over the quarter collectively reduced their review round count by 40%.
Dampeners
Not yet operating at senior scope - does not yet lead cross-team technical direction or own platform strategy.
Progression Signal
Is actively taking on work that crosses team boundaries and managing it successfully.
Business Impact
Impact
Cross-domain quality improvements and growing junior capacity contribute meaningfully to platform reliability.
Their technical contributions are beginning to be felt by analytics consumers beyond their primary domain.
Examples
A data quality framework they designed for their domain was adopted in two adjacent domains, reducing incidents platform-wide.
Dampeners
Business impact, while growing, is still primarily technical-team scoped rather than executive-visible.
Progression Signal
Business stakeholders begin to cite improved data reliability attributable to their work.
Level 4
Leading
Low
Individual
Impact
Sets the technical direction for pipeline delivery within their domain and is beginning to influence adjacent domains.
Leads data quality framework design for the team, establishing standards that junior engineers apply consistently.
Is a highly effective mentor - junior engineers in their care develop measurably faster and take on greater autonomy.
Examples
Designed a reusable data quality framework that was adopted across three data domains, reducing test authoring effort.
Mentored a junior engineer from graduate support level to independently delivering complex pipeline workstreams.
Dampeners
Cross-team technical influence is emerging but not yet fully established; working toward senior-level scope.
Progression Signal
Is being brought into cross-team architectural discussions as a credible technical voice.
Business Impact
Impact
Data quality framework contributions provide meaningful reliability guarantees to analytics consumers at scale.
Mentoring output compounds team capacity, reducing the dependency on senior engineers for pipeline delivery.
Examples
Analytics stakeholders cited a significant reduction in data quality incidents after quality standards they defined were adopted.
Dampeners
Impact is growing but still primarily team-scoped; not yet driving platform-level outcomes.
Progression Signal
Business stakeholders begin attributing improved data reliability to their work and standards.
Mid
Individual
Impact
Leads complex pipeline platform improvements - performance optimisation programmes, observability upgrades, data quality at scale.
Drives the team's data engineering standards - modelling conventions, test patterns, orchestration requirements - through practice and advocacy.
Is the senior technical mentor for junior and graduate engineers; their development outcomes reflect this individual's investment.
Examples
Led a query optimisation programme across 20 pipelines that reduced warehouse compute costs materially.
Defined the team's data contract standard and drove adoption across all pipelines in a quarter.
Dampeners
Not yet operating at full senior scope; cross-team architecture influence developing but not yet consistently senior-level.
Progression Signal
Is being considered for senior data engineer discussions; operating just below that scope consistently.
Business Impact
Impact
Platform improvements they lead reduce operational costs and improve reliability for all data consumers.
The team's engineering standards they shape create a higher baseline for data quality across the organisation.
Examples
Warehouse compute cost reductions from the optimisation programme they led freed budget for new data platform investment.
Dampeners
Still intermediate in title; this level of business impact suggests a promotion conversation is warranted.
Progression Signal
Promotion case builds; business impact now clearly exceeds the intermediate level expectation.
High
Individual
Impact
Operating at or above the senior data engineer threshold - clear candidate for promotion.
Drives cross-team data engineering standards and is a credible technical voice in architecture discussions.
Is the defining technical mentor for the team's junior cohort and is beginning to develop intermediate engineers.
Examples
Led a cross-team data mesh domain ownership initiative that defined data product standards across four teams.
Shaped the team's lakehouse pattern adoption, presenting options and driving consensus across senior stakeholders.
Dampeners
Still operating within intermediate title; promotion is the right response to this performance level.
Progression Signal
Promotion conversation should be active; delay risks losing this engineer to a senior role elsewhere.
Business Impact
Impact
Delivering data platform impact at a level that justifies senior data engineer grade.
Cross-team contributions create measurable data reliability improvements beyond the immediate team.
Examples
Cross-team standards they drove were cited by the head of analytics as enabling faster self-serve data access.
Dampeners
Impact ceiling at intermediate level; promotion unlocks the scope to drive organisation-wide platform outcomes.
Progression Signal
Promotion is overdue; further delay is a retention risk for a high-impact technical contributor.
Level 5
Transformative
Low
Individual
Impact
Performing materially beyond the intermediate level in pipeline architecture, data quality leadership, and team development.
Has independently driven organisation-wide data engineering improvements that are now embedded practice.
Is effectively operating as a senior data engineer in all but title - leading cross-team technical direction.
Examples
Designed and drove adoption of a data quality monitoring framework now used across all data domains in the organisation.
Led a cross-team architecture review that resolved a structural data modelling inconsistency spanning four teams.
Dampeners
Impact is constrained by intermediate-level access and authority; the role is the limiting factor.
Progression Signal
Promotion should be imminent; this performance level cannot persist at intermediate grade without retention risk.
Business Impact
Impact
Delivering data platform impact typical of a senior data engineer - organisation-wide rather than domain-scoped.
Their presence materially raises the data quality and engineering standards floor across multiple teams.
Examples
Data quality improvements they led were cited in a board-level data strategy update as a key platform milestone.
Dampeners
Exceptional but bounded; promotion unlocks the authority and scope to drive further organisation-level impact.
Progression Signal
Post-promotion trajectory expected to accelerate; this individual is ready for senior-level scope.
Mid
Individual
Impact
Anomalously strong for an intermediate data engineer - operating as a senior in pipeline architecture, data quality strategy, and team leadership.
Shapes data platform direction across the organisation and is a credible voice in executive data discussions.
Sets a standard of data engineering rigour that influences the entire discipline, not just the immediate team.
Examples
Contributed a data mesh domain model proposal that was adopted as the organisation's standard for data product ownership.
Independently resolved a complex cross-team data lineage problem that senior engineers had been unable to untangle.
Dampeners
Rare performance at this level; an immediate promotion review is the appropriate response.
Progression Signal
Promotion is overdue; further delay creates a material retention risk.
Business Impact
Impact
Business value delivered through data platform contributions is disproportionate to the intermediate level.
Organisation-wide data quality and architecture improvements create compounding value for analytics consumers.
Examples
A canonical data model they defined reduced duplication and inconsistency across five separate analytics workstreams.
Dampeners
This performance level at intermediate grade reflects a process gap; promotion is the right immediate action.
Progression Signal
Post-promotion, impact expected to accelerate with access to senior-level scope and authority.
High
Individual
Impact
Performing at a level that makes the intermediate title irrelevant - they are a senior data engineer by impact and influence.
Their pipeline architecture, data quality strategy, and mentoring contributions rival those of the most experienced senior engineers.
Represents an extreme outlier in the intermediate data engineering cohort.
Examples
Designed and delivered a lakehouse migration strategy for a major data domain, independently and without escalation.
Their data quality frameworks and documentation are used as reference implementations across the entire data engineering discipline.
Dampeners
Keeping this individual at intermediate level is a significant retention risk and a performance process failure.
Progression Signal
Immediate promotion is required; the organisation risks losing this individual to a senior role elsewhere.
Business Impact
Impact
Delivering organisation-level data platform impact at an intermediate cost - an exceptional return on investment.
Recognised beyond the data engineering team as a driver of data quality and platform strategy.
Examples
Named by the Head of Data as one of the most impactful contributors to the organisation's data platform transformation.
Dampeners
This situation is a process failure; the performance and compensation system has not kept pace with this individual's growth.
Progression Signal
Promotion resolves the mismatch; continued data engineering impact expected to grow further post-promotion.