Posted 17 May, 2026
Data Engineer (ETL)
Veracity
Mumbai,Maharashtra,India
Full Time
Reference: 365_621153_25-02734
Position: Data Engineer (ETL)
Experience: 5+years
Work Mode: Hybrid
Notice Period: Immediate
Location: Mumbai (Thane and Goregaon), based out in Mumbai.
Shift: General Shift (but at times there will be over lapping of time as per client timings).
Interview rounds: Total 3 rounds of interview
1. Technical Skills
Must have key skills –
• ETL Tools (IICS, Talend, Hadoop etc.)
• Azure, with basic understanding of GCP & AWS,
• JIRA Confluence,
• MS Visio (SQL / PLSQL / Oracle),
• PySpark,
• Shell script and Power BI
Data Cleaning Tools & Libraries: Proficiency with tools and libraries to clean and pre process data
for example:
• Python
• SQL
• Excel- emphasis on Familiarity with data cleaning functions, filters, and pivot tables.
Good to have skills –
• Knowledge of R
2. Data Management & Analysis Skills
• Data Validation & Consistency: Ability to identify data quality issues such as duplicates,
missing values, outliers, and inconsistencies.
• Data Transformation: Experience in transforming raw data into usable formats, including
reshaping, aggregating, or normalizing data.
• Handling Missing Data: Familiarity with imputation techniques or ways to deal with incomplete
datasets.
• Data Normalization & Standardization: Ensuring uniformity in data formats, units of
measurement, and naming conventions.
• Data Aggregation: Summarizing or grouping data for analysis and ensuring that it is consistent
across all sources.
3. Knowledge of Data Quality
• Data Integrity: Understanding the importance of maintaining accurate and consistent data over time.
• Data Profiling: Identifying patterns, anomalies, and key characteristics of the dataset.
• Error Detection: Ability to find and correct errors within datasets by checking for outliers,
misclassifications, or missing values.
4. Soft Skills
• Attention to Detail: The ability to identify small inconsistencies and issues within large datasets.
• Problem-Solving: Being resourceful in resolving data issues and proposing solutions.
• Critical Thinking: Analyzing data in-depth and understanding its implications.
• Communication: Ability to explain data issues and cleaning steps to non-technical stakeholders.
5. Experience with Data Formats
• Structured Data: Familiarity with both structured (tables, databases)
• Data Sources: Ability to clean data from various sources such as spreadsheets, databases, APIs, logs,
etc.
• File Formats: Proficiency in working with common data file formats like CSV, XML, and Excel.
Statistical and Analytical Skills
• Basic Statistics: Understanding of basic statistical concepts to spot anomalies, outliers, or trends in
data.
• Data Visualization: Ability to visualize the cleaned data to identify trends and patterns (e.g., with
Power BI .
6. Automation and Scripting
• Automating Repetitive Tasks: Experience in automating data cleaning processes with scripts or
workflow automation tools.
• Batch Processing: Capability to clean data in bulk, particularly when dealing with large datasets.
Domain Experience preferably worked with product based can be an added advantage.
Experience: 5+years
Work Mode: Hybrid
Notice Period: Immediate
Location: Mumbai (Thane and Goregaon), based out in Mumbai.
Shift: General Shift (but at times there will be over lapping of time as per client timings).
Interview rounds: Total 3 rounds of interview
1. Technical Skills
Must have key skills –
• ETL Tools (IICS, Talend, Hadoop etc.)
• Azure, with basic understanding of GCP & AWS,
• JIRA Confluence,
• MS Visio (SQL / PLSQL / Oracle),
• PySpark,
• Shell script and Power BI
Data Cleaning Tools & Libraries: Proficiency with tools and libraries to clean and pre process data
for example:
• Python
• SQL
• Excel- emphasis on Familiarity with data cleaning functions, filters, and pivot tables.
Good to have skills –
• Knowledge of R
2. Data Management & Analysis Skills
• Data Validation & Consistency: Ability to identify data quality issues such as duplicates,
missing values, outliers, and inconsistencies.
• Data Transformation: Experience in transforming raw data into usable formats, including
reshaping, aggregating, or normalizing data.
• Handling Missing Data: Familiarity with imputation techniques or ways to deal with incomplete
datasets.
• Data Normalization & Standardization: Ensuring uniformity in data formats, units of
measurement, and naming conventions.
• Data Aggregation: Summarizing or grouping data for analysis and ensuring that it is consistent
across all sources.
3. Knowledge of Data Quality
• Data Integrity: Understanding the importance of maintaining accurate and consistent data over time.
• Data Profiling: Identifying patterns, anomalies, and key characteristics of the dataset.
• Error Detection: Ability to find and correct errors within datasets by checking for outliers,
misclassifications, or missing values.
4. Soft Skills
• Attention to Detail: The ability to identify small inconsistencies and issues within large datasets.
• Problem-Solving: Being resourceful in resolving data issues and proposing solutions.
• Critical Thinking: Analyzing data in-depth and understanding its implications.
• Communication: Ability to explain data issues and cleaning steps to non-technical stakeholders.
5. Experience with Data Formats
• Structured Data: Familiarity with both structured (tables, databases)
• Data Sources: Ability to clean data from various sources such as spreadsheets, databases, APIs, logs,
etc.
• File Formats: Proficiency in working with common data file formats like CSV, XML, and Excel.
Statistical and Analytical Skills
• Basic Statistics: Understanding of basic statistical concepts to spot anomalies, outliers, or trends in
data.
• Data Visualization: Ability to visualize the cleaned data to identify trends and patterns (e.g., with
Power BI .
6. Automation and Scripting
• Automating Repetitive Tasks: Experience in automating data cleaning processes with scripts or
workflow automation tools.
• Batch Processing: Capability to clean data in bulk, particularly when dealing with large datasets.
Domain Experience preferably worked with product based can be an added advantage.
