# Data Engineering Skills Test and Verified Score > A data engineering skills test with pipeline debugging scenarios, plus live design sessions and connected GitHub repos. See what proves the skill. Source: https://getlemma.io/skills/data-engineering Data engineering is judged by what happens when things go wrong: late files, duplicate events, a schema that changed overnight. Lemma records how you design for those failures, through live sessions, assessments and code you connect. How data engineering is assessed ## What data engineering competence looks like A competent data engineer builds pipelines that can run twice without doing damage. They make jobs idempotent, partition data so a single day can be reprocessed, and decide clearly between batch and streaming based on what consumers need. They model data so analysts can query it without reverse engineering the source systems. They also plan for change and failure. They validate schemas and data quality at the edges, alert on freshness and volume rather than only on job failures, and keep lineage clear enough to answer where a number came from. Cost is part of the design, not a surprise on the invoice. ### Pipeline design Idempotent jobs, backfills and partitioning that make reruns and recovery safe and routine. ### Batch and streaming Choosing between them, handling late and duplicate events, and understanding delivery guarantees. ### Data modelling Dimensional models, slowly changing dimensions and tables that analysts can query with confidence. ### Orchestration Scheduling, dependencies, retries and alerting with tools such as Airflow or Dagster. ### Data quality and contracts Schema validation, tests on freshness and volume, and agreements with upstream producers. ### Storage and cost Warehouses, lakes, file formats and the query patterns that drive storage and compute costs. ## What counts as evidence of data engineering skills? In data engineering, a polished result can hide a shaky method. Lemma records the evidence behind the method: sessions where someone followed your reasoning, an adaptive test and the repositories you link, notebooks included. Each source is listed below. See the [evidence methodology](https://getlemma.io/methodology#evidence-types) for how each source is weighed. [Live session](https://getlemma.io/product/sessions) ### Designing a pipeline under constraints A practitioner describes the sources, rough volumes and the people who consume the data. In a live session you design the pipeline and explain how it handles late data, reruns and schema changes, and they rate it. [GitHub connector](https://getlemma.io/product/passport) ### Pipeline code on GitHub Connect a repository with DAGs, transformation models or ingestion code you wrote. Lemma checks the repository exists and that your account has access to it. [Adaptive assessment](https://getlemma.io/product/assessments) ### Debugging a broken transformation Adaptive scenario questions cover modelling, orchestration and quality. At higher difficulty you fix broken pipeline or transformation code and explain the failure, scored against a rubric. [Live session](https://getlemma.io/product/sessions) ### Incident walk-through Walk a practitioner through a pipeline failure you handled: what broke, how you found it and what you changed. Their rating of your explanation is recorded as evidence. ## How is a data engineering skills test run on Lemma? The data engineering test asks 15 to 25 questions and moves up or down a level with each answer, focusing on judgement calls such as leakage, skew and bad joins. It feeds your data engineering SCI together with sessions and linked work, and the score weighs how fresh that evidence is. [How the SCI score is calculated](https://getlemma.io/product/sci)[How adaptive skills assessments work](https://getlemma.io/product/assessments)[Practise data engineering in live skill swap sessions](https://getlemma.io/product/sessions)[Which evidence types count](https://getlemma.io/methodology#evidence-types)[What your Skills Passport shows](https://getlemma.io/product/passport) 1. ### Practise core patterns The AI tutor sets exercises on idempotency, partitioning, modelling and SQL transformations, adapting as you answer. Drills are preparation and add only a small, capped amount to your practice score. 2. ### Design with a practitioner In live sessions, design or review pipelines with someone who builds them. Completion is recorded when you both join, and the practitioner's rating becomes data engineering evidence. 3. ### Complete the assessment Scenario questions on pipelines, modelling and quality adapt to your level, with debug challenges as difficulty rises. Per-question time limits and no back-navigation keep the result honest. ## Data engineering skills: common questions ### How do I move from data analyst to data engineer and prove it? Show engineering habits on top of your SQL. Build a small pipeline with orchestration, tests and idempotent loads, connect the repository, and design pipelines live with practitioners who rate your reasoning. Pass the adaptive assessment as well. Your Passport then shows both your analysis record and new engineering evidence. ### What do data engineering interviews focus on? Expect pipeline design questions, data modelling, SQL, and how you handle failures such as late data or duplicates. Interviewers also ask about orchestration and cost. Lemma's sessions use the same kinds of design prompts, so a rated session is useful practice and evidence at once. ### Which tools does the data engineering skill cover? The skill focuses on transferable judgement: pipeline design, modelling, orchestration, quality and cost. In sessions you can use the stack you know, whether that is Airflow, dbt, Spark or a cloud-native service. Connected repositories show your specific tooling in practice. ### Can a personal project count as data engineering evidence? Yes, if it reflects real engineering concerns. A project that ingests a public API, handles retries and late data, models the result and includes tests is worth connecting. Lemma checks the repository exists and that you have access, and practitioners can review its design in a session. ## Skills related to data engineering [Browse the skills directory](https://getlemma.io/skills) [### SQL Prove you can write correct, fast queries on real schemas, from joins to window functions and query plans. Evidence for SQL](https://getlemma.io/skills/sql)[### Python Show how you write, debug and structure Python, not just that you once finished a course. Evidence for Python](https://getlemma.io/skills/python)[### Cloud architecture Show how you design cloud systems that scale, fail gracefully and stay affordable. Evidence for cloud architecture](https://getlemma.io/skills/cloud-architecture)[### Machine learning Show how you frame, train, evaluate and debug models, with evidence a practitioner has reviewed live. Evidence for machine learning](https://getlemma.io/skills/machine-learning) ![Painting of an olive grove in soft sunlight with hills beyond](https://cdn.sanity.io/images/islu4peo/production/613eb3f93566423c9adf45246f2cf1a61437c04c-1680x1120.jpg?w=1080&fm=webp&q=68&fit=max) ## Start your data engineering evidence record. Lemma is in early access. Request an invite and we will email you when your place is ready.