Explore Data Engineer Jobs in Bangalore for entry-level candidates interested in building data pipelines, processing structured and unstructured datasets, working with databases, and supporting analytics platforms. This opportunity is suitable for fresh graduates and junior professionals with practical knowledge of SQL, Python, databases, ETL concepts, and data engineering fundamentals.
Job Information
Position: Entry-Level Data Engineer
Location: Bangalore / Bengaluru, Karnataka
Experience: 0–2 Years
Employment Type: Full-Time
Job Category: Data Engineering / IT
Salary: Open for the right candidates
Qualification: B.E. / B.Tech / BCA / MCA / B.Sc / M.Sc in Computer Science, IT, Data Science or related discipline
Core Skills: SQL, Python, ETL, Databases & Data Pipelines
About the Role
As an Entry-Level Data Engineer, you will work with data and engineering teams to collect, transform, validate, and prepare data for analytics and business applications.
You will assist experienced engineers in developing reliable data pipelines, writing SQL queries, automating data-processing tasks, monitoring data workflows, and resolving basic data-quality issues.
This role is suitable for candidates who enjoy working with data, programming, databases, and problem solving.
Key Responsibilities
- Assist in developing and maintaining data pipelines.
- Write and optimize SQL queries.
- Extract data from databases, APIs, files, and other sources.
- Transform raw data into usable datasets.
- Support ETL/ELT workflows.
- Perform data validation and quality checks.
- Identify missing, duplicate, or inconsistent data.
- Assist with data integration between different systems.
- Write Python scripts for basic data-processing tasks.
- Monitor scheduled data jobs and pipeline executions.
- Troubleshoot basic pipeline failures.
- Maintain data-processing documentation.
- Work with analysts, developers, and senior data engineers.
- Follow data security and access-control procedures.
- Learn and apply data-engineering standards and best practices.
Required Skills
Candidates should have a good foundation in:
SQL: SELECT statements, joins, aggregations, subqueries, filtering, grouping, and basic query optimization.
Python: Variables, functions, loops, data structures, file processing, error handling, and basic data manipulation.
Databases: Understanding of relational databases, tables, schemas, primary keys, foreign keys, and data types.
ETL/ELT: Basic understanding of extracting, transforming, and loading data between systems.
Data Fundamentals: Ability to understand structured data, data quality, transformations, and basic data modelling.
Python for Data Engineering
Candidates should be comfortable writing basic Python programs for data-processing activities.
Exposure to libraries such as Pandas, NumPy, or similar data-processing tools can be useful.
Entry-level candidates are not expected to be advanced Python developers. However, they should be able to read existing scripts, make simple changes, process files, connect to data sources, and troubleshoot basic errors.
SQL & Database Knowledge
SQL is an important skill for this position.
Candidates should be able to work with databases such as MySQL, PostgreSQL, SQL Server, Oracle, or similar relational systems.
Practical SQL exercises during interviews may involve:
- Retrieving data from tables.
- Using INNER and LEFT JOIN.
- Filtering records.
- GROUP BY and aggregate functions.
- Subqueries.
- Removing duplicate results.
- Working with dates.
- Understanding indexes at a basic level.
ETL & Data Pipelines
Candidates should understand how data moves from a source system to a destination.
A basic pipeline may involve:
Source → Extraction → Transformation → Validation → Storage → Analytics
Exposure to tools such as Apache Airflow, Azure Data Factory, AWS Glue, Informatica, Talend, or similar technologies can strengthen a candidate’s profile but may not be mandatory for an entry-level role.
Big Data Exposure
Basic knowledge of Apache Spark, PySpark, Hadoop, or distributed data-processing concepts can be an advantage.
For an entry-level position, strong SQL, Python, database, and data-pipeline fundamentals are generally more important than listing many big-data technologies without practical knowledge.
Cloud Fundamentals
Exposure to AWS, Microsoft Azure, or Google Cloud Platform can be beneficial.
Useful introductory knowledge may include cloud storage, databases, compute services, data-processing services, permissions, and basic cloud architecture.
Candidates with academic or personal cloud-data projects should include them on their resumes.
Data Warehousing Fundamentals
Basic knowledge of data warehousing can strengthen an entry-level Data Engineer profile.
Candidates should understand concepts such as:
Data Warehouse, Data Lake, Fact Tables, Dimension Tables, Schema, Data Mart, Structured Data and Data Modelling.
Exposure to platforms such as Snowflake, Amazon Redshift, Google BigQuery, Azure Synapse, or similar technologies is an advantage but not mandatory for every opening.
Who Can Apply?
This role is suitable for:
- Fresh graduates interested in data engineering.
- Candidates with 0–2 years of relevant experience.
- Software or database trainees moving into data engineering.
- Candidates with internships in data, analytics, cloud, or software development.
- Candidates who have completed practical SQL and Python projects.
- Junior professionals looking to build a career in data engineering.
Candidates should be able to demonstrate practical learning through projects, internships, labs, GitHub repositories, or relevant coursework.
Good Entry-Level Projects to Highlight
Candidates can strengthen their resumes by demonstrating projects such as:
- Building a Python and SQL ETL pipeline.
- Processing CSV or JSON datasets.
- Moving data between databases.
- Creating automated data-cleaning scripts.
- Building a simple Airflow pipeline.
- Processing datasets using PySpark.
- Creating a cloud-based data pipeline.
- Loading transformed data into a warehouse.
The ability to clearly explain how a project works is more valuable than simply listing multiple technologies.
Preferred Technical Skills
Programming: Python
Query Language: SQL
Databases: MySQL, PostgreSQL, SQL Server or Oracle
Data Processing: Pandas, NumPy
ETL Concepts: Extraction, Transformation, Loading
Big Data Exposure: Spark, PySpark
Cloud Exposure: AWS, Azure or GCP
Data Warehousing: Snowflake, Redshift, BigQuery or Synapse exposure
Workflow Tools: Airflow or similar tools
Version Control: Git
Candidates are not expected to know all these technologies for an entry-level position.
What to Highlight on Your Resume
For an entry-level Data Engineer resume, focus on actual technical ability.
Highlight your SQL skills, Python projects, database knowledge, ETL projects, internships, data-processing work, cloud exposure, GitHub projects, certifications, and academic projects.
Avoid filling your resume with technologies you cannot explain during a technical interview.
Apply for Entry-Level Data Engineer Jobs in Bangalore
Candidates searching for Data Engineer Jobs in Bangalore at the entry level can apply for opportunities involving SQL, Python, databases, ETL, data pipelines, cloud platforms, and data-processing systems.
The selection process may include assessments covering SQL queries, Python fundamentals, database concepts, ETL, data modelling, problem solving, and basic data-engineering scenarios.
Salary is open for the right candidates and will depend on technical skills, internship or project experience, educational background, interview performance, and individual employer requirements.