Hadoop Developer at Persistent System Ltd · Bengaluru (Bangalore), Pune, Hyderabad · 4 - 6 years · ₹6L - ₹22L / yr · Posted 15 Jul 2022
Location: Bangalore/Pune/Hyderabad/Nagpur
4-5 years of overall experience in software development.
- Experience on Hadoop (Apache/Cloudera/Hortonworks) and/or other Map Reduce Platforms
- Experience on Hive, Pig, Sqoop, Flume and/or Mahout
- Experience on NO-SQL – HBase, Cassandra, MongoDB
- Hands on experience with Spark development, Knowledge of Storm, Kafka, Scala
- Good knowledge of Java
- Good background of Configuration Management/Ticketing systems like Maven/Ant/JIRA etc.
- Knowledge around any Data Integration and/or EDW tools is plus
- Good to have knowledge of using Python/Perl/Shell
Please note - Hbase hive and spark are must.

Similar jobs (4)
Dear Candidate,
Thanks for showing interest in the opportunity!!!
As discussed, we have an opening for ETL Developer – Hadoop & PySpark with Mphasis for the Bangalore location – Permanent.
Job Description:
We are looking for an experienced ETL Developer with strong expertise in ETL development, Hadoop, and PySpark. The candidate should have hands-on experience in data processing, building and maintaining ETL pipelines, data transformation, and handling large datasets using Big Data technologies.
Primary & Mandatory Skills:
- ETL Development
- Hadoop / HDFS
- PySpark / Apache Spark
- Data Extraction, Transformation & Loading
- SQL and Data Processing
Good to Have:
- Hive / Spark SQL
- Data Pipeline Development
- Big Data Processing
- Python Programming
Company: Mphasis
Role: ETL Developer – Hadoop & PySpark
Experience: As per requirement
Location: Bangalore
Employment Type: Permanent
Job Description
We are looking for an experienced ETL Developer with strong expertise in ETL development, Hadoop, PySpark, Python, and SQL. The candidate should have hands-on experience in developing data pipelines, processing and transforming large datasets, and working with data warehousing and big data technologies.
Key Responsibilities
- Design, develop, and maintain ETL processes and data pipelines.
- Work with Hadoop and PySpark to process and transform large volumes of data.
- Develop and optimize Python scripts for data processing and automation.
- Write complex SQL queries for data extraction, transformation, and validation.
- Perform data cleansing, integration, transformation, and validation.
- Work with data warehousing concepts and large-scale data processing systems.
- Optimize ETL workflows for performance, scalability, and reliability.
- Troubleshoot data pipeline failures and resolve data quality issues.
- Collaborate with data engineers, analysts, and other technical teams.
- Follow data governance, quality, and development best practices.
Required Skills
- Strong experience in ETL Development – Mandatory
- Hands-on experience with Hadoop – Mandatory
- Strong experience in PySpark – Mandatory
- Good programming skills in Python – Mandatory
- Strong knowledge of SQL – Mandatory
- Experience in Data Processing and Transformation – Mandatory
- Knowledge of Data Warehousing – Mandatory
- Experience with Big Data Technologies – Mandatory
Opportunity Details
Role: ETL Developer – Hadoop & PySpark
Experience: 6–10 Years
Location: Bangalore
Mandatory Skills
ETL Development | Hadoop | PySpark | Python | SQL | Data Processing & Transformation | Data Warehousing | Big Data Technologies
Job Title: Java + Apache Spark Developer
Experience: 5–10 Years
Location: Mumbai
Work Mode: Hybrid (3 Days WFO)
Job Description:
Looking for hands-on Java Developers with strong experience in Apache Spark and distributed data processing. Candidates should have experience building and optimizing data pipelines, working with Spark SQL, and using Snowflake or Databricks for enterprise data engineering solutions.
Mandatory Skills:
Java, Apache Spark, Spark SQL, Snowflake / Databricks, SQL, ETL.
Hiring: ETL Developer – Hadoop & PySpark
Location: Bangalore
Experience: 6–10 Years
Budget: 25–30 LPA
Work Mode: Weekdays
Job Description
We are looking for an experienced ETL Developer with strong expertise in Hadoop, PySpark, Python, and SQL to design, develop, and optimize data processing and transformation solutions. The ideal candidate should have hands-on experience working with big data technologies, building scalable ETL pipelines, and managing data for data warehousing and analytics.
Key Responsibilities
- Design, develop, and maintain scalable ETL pipelines for large-scale data processing.
- Develop data transformation workflows using PySpark and Python.
- Work with Hadoop and other big data technologies to process and manage large datasets.
- Write complex SQL queries for data extraction, transformation, validation, and analysis.
- Implement data cleansing, data quality checks, and data validation processes.
- Develop and optimize data integration workflows for data warehousing solutions.
- Troubleshoot ETL failures, performance issues, and data discrepancies.
- Optimize PySpark jobs and SQL queries for performance and scalability.
- Collaborate with data engineers, architects, analysts, and other stakeholders to deliver reliable data solutions.
- Ensure data accuracy, consistency, and adherence to established data governance standards.
Mandatory Skills
- Strong hands-on experience in ETL development.
- Good experience with Hadoop and big data technologies.
- Proficiency in PySpark and Python programming.
- Strong SQL skills, including complex queries and data manipulation.
- Experience in data processing, data transformation, and data integration.
- Understanding of data warehousing concepts and architecture.
- Experience in developing, debugging, and optimizing ETL pipelines.
Required Experience
- 6–10 years of relevant experience in ETL development and data engineering.
- Practical experience handling large datasets using Hadoop and PySpark.
- Strong analytical, problem-solving, and troubleshooting skills.
- Ability to work independently and collaborate effectively within a team.
Interested candidates can share their updated resumes along with current CTC, expected CTC, notice period, and total relevant experience.








