Build depth in the four areas companies test most: SQL (joins, window functions, query optimization), PySpark, one cloud platform (Azure, GCP or AWS), and data warehousing/modelling basics. Effective data engineering interview preparation usually looks like this — get hands-on with SQL and PySpark first, build 1–2 end-to-end pipeline projects, revise ETL concepts and orchestration tools like Azure Data Factory or Airflow, and finish with mock interviews to fix communication gaps. Avoid theory-only preparation; most rounds now make you write live queries or design a pipeline on the spot.

Freshers are typically asked the difference between OLTP and OLAP, ETL vs ELT, batch vs streaming, structured vs unstructured data, SQL questions (joins, GROUP BY, window functions) and basic Python. Expect a walkthrough of any project on your resume — what pipeline you built, why you chose those tools, and what went wrong. Since freshers don't have work experience, projects and SQL depth decide most fresher rounds.

With 3+ years of experience, questions shift to real-world depth: optimizing a slow pipeline, handling late-arriving or duplicate data, incremental loading strategies, partitioning and file formats like Parquet, data modelling for analytics, and designing an end-to-end system on a cloud platform. Interviewers also probe "tell me about a production issue you faced" to test ownership. Be ready to explain architecture trade-offs from your actual work, not textbook definitions.

Clarify the expected output and edge cases first, think aloud while writing, start with a simple working query and then optimize it using joins, CTEs or window functions where they are cleaner. If you forget exact syntax, explain the logic — interviewers assess your reasoning, not your memory. A common mistake is solving silently; narrate your approach as you go.

Prepare joins (inner, left, self), WHERE vs HAVING, GROUP BY with aggregates, primary and foreign keys, subqueries, and basic window functions like ROW_NUMBER and RANK. Classics like "find the second-highest salary" or "find duplicates in a table" appear in almost every fresher round in India. Practise on a live SQL editor because many companies now ask candidates to run queries in real time.

At that level, expect optimization and scenario-based questions: complex window functions (running totals, deduplication, gaps and islands), recursive CTEs, execution plans, indexing strategy, and handling very large tables. You will often be asked to rewrite an inefficient query and explain why your version scales better. Depth of reasoning, not the number of queries solved, separates senior candidates.

Azure Data Factory is Microsoft's cloud service for building ETL/ELT pipelines — it ingests data from sources like databases, files and APIs, transforms it, and loads it into a warehouse or data lake. It works through pipelines made of activities: copy activities move data, data flows handle transformations on managed Spark compute, and triggers schedule execution. It is usually the first tool data engineers learn on the Azure stack.

They solve different problems — Azure Data Factory is an orchestration and data-movement tool with low-code connectors, while Databricks is a Spark-based platform for heavy transformations and analytics using PySpark, SQL or Scala. Learn ADF basics first to understand orchestration, then go deep on Databricks because PySpark skills carry more weight in interviews. Most production teams use both in the same pipeline, so they complement rather than replace each other.

The frequent ones include: pipeline vs activity vs trigger; types of integration runtimes and when to use each; copy activity vs mapping data flow; schedule vs tumbling window triggers; incremental loading using watermarks or change tracking; error handling, retries and debugging; and monitoring pipeline runs. Interviewers often end with a design task — "build a pipeline that loads data from source X into a warehouse daily" — so practise designing, not just theory.

Create a free Azure account, follow the official learning path, and build small things yourself — copy a CSV from Blob Storage into a SQL database, add a data flow, then schedule it with a trigger. Next, learn integration runtimes, parameters and incremental loads. Pair it with SQL and basic Python so you finish with a small end-to-end project you can confidently discuss in interviews.

ADF is pay-as-you-go: you pay for orchestration activity runs, data movement (based on DIU-hours), and data flow execution on Spark clusters. Learning-scale usage usually costs very little, but data flow clusters and frequent trigger runs can add up, so delete unused pipelines and set spending alerts in the Azure portal. Always check current rates on the official pricing page since they change over time.

No certification covers only Azure Data Factory — ADF skills are included within the broader Azure data engineering certification paths, such as the Azure Data Engineer Associate track. Preparing for that exam while building pipelines in a free account is the standard route. Interviewers care more about whether you can actually design and debug pipelines than about the certificate itself.

Tie genuine interest to evidence: mention what pulled you toward data (a project, a problem you enjoyed solving), show you understand what the job actually involves, and connect it to the company's data scale or stack. Avoid vague lines like "data is the future". A simple structure works well: what sparked the interest → what you have done to build the skill → what you want to build next in the role.

Netflix data engineering rounds go deep on advanced SQL, data modelling and pipeline/system design, with detailed probing of your past projects and their business impact. Prepare to design an end-to-end solution — ingestion, transformation, orchestration and monitoring — and defend your trade-offs. Revise Spark internals and distributed processing fundamentals, and do several mock interviews before applying, since the bar for depth is higher than at most companies.

GitHub has multiple community-maintained repositories that compile data engineering interview questions, SQL exercises and system design case studies — search for "data engineering interview" and pick repos that are recently updated and well-starred. Use them as a checklist rather than an answer bank: verify each concept yourself and practise writing real queries and pipeline designs, because interviewers easily spot rote preparation.