Junior Data Engineer | Role Archetype

Building and maintaining ETL/ELT pipelines with growing independence, developing data modelling fundamentals, and contributing meaningfully to the team's data platform.

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Growing into this role

Graduate Data Engineer to Junior Data Engineer

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Next step pathway

Junior Data Engineer to Intermediate Data Engineer

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Performance Calibration

Junior Data Engineer - Performance Levels

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Behaviour Tracker

Junior Data Engineer - Performance Tracker

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Overview

As a Junior Data Engineer, you are moving beyond pure learning mode and starting to deliver meaningful contributions to the team's data platform. You build and maintain ETL/ELT pipelines, work with data warehouses, and grow your understanding of data modelling - increasingly with less moment-to-moment guidance, though still with clear direction and close support.

You are expected to complete well-defined tasks independently, raise blockers early, and bring increasing rigour to the quality of your data work. You are beginning to develop instincts about data quality, pipeline reliability, and the downstream impact of the systems you build.

Key Responsibilities

Pipeline Development

Build and maintain ETL/ELT pipelines under the direction of an intermediate or senior data engineer.
Write SQL transformations and Python scripts that are clean, tested, and documented.
Follow the team's standards for pipeline structure, naming conventions, and data modelling patterns.
Participate in the design of small pipeline components, contributing ideas and flagging concerns.
Maintain and improve existing pipelines - fixing data issues, improving performance, and updating for schema changes.

Data Quality

Apply data quality checks and assertions to pipelines you build or modify.
Investigate data quality alerts and anomalies, escalating to a senior engineer when appropriate.
Document data quality expectations clearly so downstream consumers understand what guarantees the pipeline provides.
Develop understanding of how upstream data changes ripple through to downstream consumers.

Delivery

Deliver well-defined tasks independently within agreed timeframes.
Participate meaningfully in code reviews - both giving and receiving feedback.
Raise blockers clearly and early, with enough context for your TTL or senior to help effectively.
Keep your work visible to the team through consistent task board updates and clear PR descriptions.

Collaboration and Learning

Engage constructively in team ceremonies and contribute ideas in planning and retrospectives.
Build relationships with data analysts and business stakeholders to understand how data is consumed.
Continue developing your technical skills through reading, internal learning, and on-the-job exposure.
Provide helpful, specific feedback in code reviews on other junior or graduate engineers' work.

Role Specific

ETL/ELT Pipeline Delivery

Build and maintain data pipelines that reliably ingest, transform, and load data - applying the team's tooling, conventions, and quality standards consistently.

Data Modelling Fundamentals

Develop working knowledge of dimensional modelling, normalisation, and the team's preferred data warehouse patterns - applying them in delivered work with guidance from senior engineers.

Data Warehouse Proficiency

Build practical proficiency in the team's cloud data warehouse - understanding how to structure queries efficiently, manage storage patterns, and use the platform's features appropriately.

Behaviours

Learning & Growth

Actively seeks to deepen understanding of data modelling, pipeline patterns, and the business domain they are serving.
Reflects on feedback from code reviews and applies lessons to subsequent work without being reminded.
Reads data engineering literature, follows community discussions, and brings relevant ideas back to the team.
Develops awareness of their own knowledge gaps and proactively raises them with their TTL or mentor.
Takes on moderately stretching tasks and uses them as development opportunities rather than defaulting to familiar approaches.
Builds understanding of the business context behind the data they work with - not just the technical pipeline.

Delivery

Delivers well-defined pipeline and data tasks independently within agreed timeframes.
Manages their own task queue effectively - breaking down work, estimating effort, and flagging when estimates change.
Raises blockers promptly with enough context for a senior to help efficiently.
Keeps PRs reviewable - appropriately scoped, with clear descriptions and evidence of testing.
Responds to review feedback promptly and addresses it thoroughly before requesting re-review.
Tracks task status accurately so the team always has a clear picture of progress.
Contributes to planning by providing thoughtful estimates and flagging risks they are aware of.

Quality & Craft

Writes SQL and Python that is clean, readable, and follows team conventions without needing to be reminded.
Applies appropriate data quality tests to all pipeline work - not just as box-ticking but as genuine protection for downstream consumers.
Reviews own work critically before submitting - checking for edge cases, null handling, and performance concerns.
Writes clear documentation for pipelines, transformations, and data models so that others can understand and maintain them.
Identifies and flags technical debt encountered during delivery work, even when not expected to resolve it immediately.
Develops growing instinct for pipeline performance - identifying queries that will not scale and raising concerns early.

Communication

Provides clear, specific stand-up updates that give teammates a genuine picture of progress and blockers.
Writes PR descriptions that explain what changed, why it changed, and how to verify the outcome.
Communicates data quality concerns clearly to senior engineers - with evidence, not just intuition.
Asks focused, well-formed questions that show prior investigation rather than asking before attempting.
Documents decisions and assumptions in pipelines and data models so future engineers understand the reasoning.
Gives constructive, specific feedback in code reviews on peers' work.

Collaboration

Builds effective working relationships with data analysts and business stakeholders to understand how data is consumed.
Contributes constructively to team ceremonies - retrospectives, planning, and technical discussions.
Offers meaningful code review feedback to graduate engineers, balancing rigour with encouragement.
Shares knowledge with teammates - new tools, useful patterns, lessons from debugging - without being asked.
Works openly rather than siloing work in progress - makes it easy for others to see and assist.
Engages positively with cross-team collaboration, treating other teams' needs with the same respect as the team's own.

Ownership

Takes full responsibility for completing tasks they have committed to, including follow-through on review actions.
Flags uncertainty about data requirements or business logic early rather than making assumptions.
Maintains data quality in areas they own - investigating alerts, fixing issues, and preventing recurrence.
Proactively monitors pipelines they have built or modified, not just during development but after deployment.
Owns their own learning progression and actively manages it, seeking feedback and opportunities.
Acknowledges mistakes clearly, investigates root causes, and shares learnings with the team.

Technical Foundation

Demonstrates solid SQL and Python proficiency in all delivered work.
Applies data quality testing practices consistently - tests are part of the definition of done, not an afterthought.
Uses the team's orchestration tool effectively - writing clear DAGs or workflow definitions with appropriate dependencies and error handling.
Understands dimensional modelling concepts well enough to contribute to data model design conversations.
Navigates the cloud data warehouse confidently - structuring efficient queries and understanding partitioning and clustering basics.
Understands the data lifecycle - ingestion, transformation, serving - and where their work fits within it.
Maintains working knowledge of the team's deployment and orchestration patterns.

Skills

Solid SQL skills - complex joins, CTEs, window functions, aggregations - applied in a cloud data warehouse environment.

Working Python proficiency for pipeline scripting, data transformation, and basic automation.

Familiarity with a data orchestration tool (e.g. Airflow, dbt, Prefect) in a practical delivery context.

Basic understanding of dimensional modelling - facts, dimensions, slowly changing dimensions.

Understanding of data quality testing patterns - null checks, uniqueness, referential integrity, freshness.

Version control proficiency - branching, pull requests, resolving conflicts.

Growing familiarity with cloud data platforms (e.g. BigQuery, Snowflake, Redshift, Databricks).

AI AI & Automation Expectations Updated for the AI-augmented era

AI Augmented Delivery

Uses AI coding assistants to accelerate SQL and Python development - generating query skeletons, dbt model stubs, and transformation logic - while validating outputs against the actual data schema.
Reviews AI-generated pipeline code for correctness - checking join logic, aggregation behaviour, and filter conditions against known data characteristics before committing.
Uses AI to help write data quality tests and assertions, then verifies that the generated tests would actually catch the failure modes they are intended to detect.
Recognises that AI is particularly prone to errors in SQL involving NULLs, fan-out joins, and aggregation over sparse data - applies deliberate scrutiny to these areas.
Uses AI to help understand unfamiliar data modelling patterns, warehouse-specific SQL dialects, and orchestration tool APIs.
Develops prompt discipline - providing table schemas, row counts, and business context when asking AI to generate transformations, to reduce hallucination and improve output relevance.