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Data engineer job description template

Copy a fresher data engineer job description covering SQL, Python, pipelines and data-quality checks. Includes an intern version, screening questions on failed loads and repeat runs, and guidance on shortlisting.

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In short

A fresher data engineer writes SQL and Python jobs that move data from files, APIs and databases into usable tables. They check completeness, investigate failed loads and document changes under a senior’s review. Ask for SQL, Python, Git and a small pipeline they can explain, including how it handles failures and repeat runs. Name specialist tools only when the role uses them.

Data engineer job description template

Fill in your company details once and both versions below update: the full-time job for freshers and the internship. Copy the one you need into your job post.

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1. Full-time data engineer (fresher)

Data engineer (fresher)

Company: [Company name]
Location: [Location] · Work mode: [Work mode]
Employment type: Full-time
CTC: [CTC for the full-time role]
Joining: [Joining date] · Apply by: [Apply by]

About [Company name]

[About your company]

About the role

[Company name] is hiring a fresher data engineer for the [Team] team to build and maintain small data pipelines. You will write SQL and Python jobs that bring data from files, applications and databases into our warehouse, check that each load is complete and fix it when it fails. Analysts and managers rely on these tables every morning, so a senior engineer will review each change and teach you how we test before anything reaches them.

What you will do

  • Write SQL and Python jobs that extract data from files, databases and APIs and load it into the warehouse or database, with a senior reviewing each pipeline
  • Read and clean incoming CSV, JSON and Parquet files by fixing data types, dates and encodings, and set aside rows that fail checks together with the reason
  • Make loads safe to repeat using the team’s agreed keys and update rules, check for duplicate source keys before an upsert or merge, and test that repeating the same input after a partial failure leaves the expected rows and values
  • Schedule jobs in Airflow or cron, add retries and failure alerts, and keep jobs that depend on each other in the right order
  • Add data-quality checks after each load: row counts against the source, null and duplicate checks on key columns, and a check that yesterday’s data has arrived
  • Investigate failed or late loads from the logs, find the cause, such as a renamed column, a missing file or a timeout, fix it or escalate, and write a short incident note [If applicable: you will join the out-of-hours rota after shadowing a senior]
  • Write SQL transformations that turn raw tables into clean ones for analysts, and compare their totals with the source before release
  • Run a backfill for past dates when a senior asks, and confirm that the loaded row counts match the source and that no day is loaded twice
  • Document each table and pipeline: what the columns mean, where the data comes from, when it refreshes and who owns it
  • Handle requests from analysts, such as a missing column or a total that does not match, and track each one in the team’s ticket tool

What we are looking for

  • SQL beyond the basics: joins, GROUP BY, subqueries and common table expressions, on PostgreSQL, MySQL or a similar database
  • Python for scripts: reading and writing files, dictionaries, error handling, and pandas for cleaning CSV and JSON
  • Relational database basics: primary and foreign keys, indexes and why tables are normalised
  • A clear idea of extract, transform and load, and of the difference between a full load and an incremental load
  • Git and the Linux command line: running a script, reading a log file and finding the line that failed
  • Care with numbers: you compare row counts and totals before and after a load, and report a mismatch instead of ignoring it
  • One small pipeline you built, such as a script that downloads a public file, cleans it and loads it into a database. Share the repository with a README, or walk through the steps if the work is private

Good to have

  • Apache Airflow: a small workflow (DAG) you wrote or ran, even on your laptop
  • Hands-on use of a cloud warehouse such as BigQuery, Snowflake or Redshift
  • Parquet and the pyarrow library, and how a column-oriented format differs from CSV for large files
  • dbt basics: a model, a test and a documentation page
  • Docker basics for running a database or a job on your laptop

Who can apply

Final-year students and graduates of B.E./B.Tech in computer science, IT or electronics fit well, as do BCA, MCA, B.Sc and M.Sc candidates in computer science, statistics or mathematics. Candidates from other streams are equally welcome. Judge every applicant on the requirements above, not the degree.

How to apply

Apply through this job post by [Apply by]. Shortlisted candidates will hear from us about the next steps.

2. Data engineer internship

Data engineer intern

Company: [Company name]
Location: [Location] · Work mode: [Work mode]
Employment type: Internship · Duration: [Internship duration]
Stipend: [Stipend for the internship]
Joining: [Joining date] · Apply by: [Apply by]

About [Company name]

[About your company]

About the role

[Company name] offers a data engineering internship for students who want to see how data reaches the reports teams use each day. You will work with a mentor on one small pipeline: loading files into a database, checking the data and reading logs when a run fails. You will finish by presenting the pipeline and a one-page guide to running and repairing it.

What you will do

  • Load a folder of sample or masked CSV files into a PostgreSQL table with a Python script, and record how many rows loaded and how many were rejected
  • Make that script safe to run twice on the same input, then compare keys, row counts and stored values after both runs to confirm that the result is unchanged
  • Write SQL checks that find duplicate keys, blank required columns and missing dates in a table
  • Read the log of a failed run in the test environment and write down what failed and how it was fixed
  • Convert one large CSV file to Parquet and note the change in file size and read time
  • Document one table with its columns, source and refresh time, and present the pipeline to the team

What we are looking for

  • Pursuing a degree in computer science, IT, statistics or a related subject, or recently graduated
  • SQL from coursework: joins, grouping and filters
  • Python scripts that read a file and write results, with basic error handling
  • A small project that moves data from a file or an API into a database, shared on GitHub or described if it is private
  • Willing to read logs and error messages patiently and to ask when a failure stays unclear

Who can apply

Final-year students and graduates of B.E./B.Tech in computer science, IT or electronics fit well, as do BCA, MCA, B.Sc and M.Sc candidates in computer science, statistics or mathematics. Candidates from other streams are equally welcome. Judge every applicant on the requirements above, not the degree.

How to apply

Apply through this job post by [Apply by]. Shortlisted candidates will hear from us about the next steps.

Other titles for this role

Junior data engineer, Associate data engineer, Data engineer trainee, ETL developer (fresher), Data pipeline engineer (fresher).

Screening questions to add to your job post

  1. A load failed halfway through. How do you re-run it safely without creating duplicates? Look for a repeatable load strategy, such as staging followed by an upsert on agreed keys, or replacing the affected date’s rows in one transaction. A good answer checks duplicate keys, row counts and stored values after the rerun. Ask how the candidate would confirm that a partial failure has not changed the intended result.
  2. How do you find duplicate rows in a table? Look for grouping by the key columns and keeping the groups with a count above one, or numbering the rows within each key. A good extra is asking what counts as a duplicate here: the same key or the whole row.
  3. You join orders to customers and the row count drops. What is the likely cause, and what would a left join from orders to customers change? Look for an inner join dropping orders that have no matching customer, and a left join from orders retaining unmatched orders with NULL values in the customer columns. If the candidate mixes up the two joins, ask them to work through a three-row example.
  4. What is a primary key, and why does a pipeline care about it? Look for a column or set of columns that identifies each row uniquely, and its use in joins, duplicate checks and upserts. If the answer stops at the definition, ask where a load would use the key.
  5. How would you check that yesterday’s load is complete? Look for a row count against the source, the latest date or timestamp loaded, null checks on key columns and an alert when a check fails. If the candidate would look at a few rows by eye, ask what else they could check.
  6. A daily file arrives with a new column and dates in a different format. What should the pipeline do? Look for detecting the change, avoiding loading wrong values and telling the file’s owner. Accept stopping the load, setting the file aside, or loading only the known columns while rejecting the unexpected dates. A good extra is standardising the date format in one place and checking a few known rows afterwards.

What to look for when you shortlist

  • A project in which data moves from a source to a database, with the run steps in a README or explained in a walkthrough if the work is private
  • A script that gives the same result when run twice, and a candidate who can explain why
  • Checks inside the project, such as row counts, duplicate tests and a file of rejected rows
  • Readable SQL with clear names, one step per common table expression and comments where the logic is not obvious
  • Sample data with real mess in it: blank keys, mixed date formats, extra header rows and odd encodings
  • A plain note of what the project loaded, such as the source, the row count and the run time. Small data is fine.

Mistakes to avoid in this job description

  • Asking a fresher for Hadoop, Spark, Kafka and a cloud warehouse together: keep SQL and Python as the must-haves, name the one warehouse and the one scheduler you actually run, and say you will train on the rest.
  • Not stating the size of the data or the tools: say how many source systems feed the warehouse, roughly how many rows arrive each day and which database and scheduler you use, so candidates can tell a small-team post from a large-platform one.
  • Confusing the role with an analyst’s: if most of the work is dashboards and ad-hoc queries, use the data analyst template, because a data engineer’s output is tables other people can trust.
  • Leaving out on-call expectations: if a failed overnight load means a call or a weekend check, say so, say who backs up the fresher, and let the new joiner shadow a senior first.
  • Not saying what data the fresher may see: name the tables and fields the fresher can read, mask personal columns in test data and give an intern sample data only, so nobody works on customer or employee records by accident. Follow your company’s data policy.

Questions about hiring a data engineer

What should a fresher data engineer know?

SQL beyond SELECT, Python scripting, relational database basics, the idea of extract, transform and load, Git and the command line. Add the habit of checking row counts after every load. A good proof is a small pipeline that the candidate can walk you through, including what happens when it runs twice.

How is a data engineer different from a data analyst or a data scientist?

The data engineer builds and maintains the pipelines and tables, and fixes them when loads fail. A data analyst uses data, including those tables, to answer business questions and build reports. A data scientist builds statistical and machine learning models, and prepares some of the data for them. In a small team one fresher may cover two of these, so state which comes first.

Which tools should I name in a fresher data engineer post?

Name the ones you run today: the database or warehouse, the scheduler, the file formats and the cloud, if any. Then say which the fresher needs on day one and which you will teach. Accept close equivalents, such as cron experience for an Airflow post, and teach the rest. Name Spark only if your team runs it today, and say whether the fresher will use it in the first months.

How can I test a fresher data engineer quickly?

Create a small fictional CSV with a repeated row, a blank key and mixed date formats. Supply the expected format for each source and include an ambiguous date that should be flagged rather than guessed. Ask for a repeatable load, duplicate checks and a note explaining rejected rows. Set the time limit and marking criteria before the task, and accept a request for clarification.

What does a fresher data engineer do in the first three months?

In month one, give read-only access to the tables the work needs, with personal columns masked where your data policy requires it, and let the fresher learn the sources, tables and scheduler by diagnosing small failures beside a senior. In month two, hand over a small pipeline in a test schema. By month three, expect a reviewed change in production with checks added. Widen write access step by step.

What project suits a data engineering intern?

Choose one pipeline with a clear finish: load a set of masked or sample files into a database, make the load safe to repeat, add duplicate and row-count checks, and document the table. Give the intern a test schema, a mentor and a demo day. LineupX reaches students across 100+ institutions and edtechs, and posting the internship there is free.

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