Job Title: Senior Data Quality EngineerExperience: 6–8 YearsJob Description:• Own end-to-end data quality validation across ingestion, transformation, and consumption layers• Design and maintain ETL automation frameworks using Python and Pytest• Perform source-to-target validation, reconciliation, and anomaly detection• Validate cloud-based data architectures (data lakes, warehouses, streaming platforms)• Implement data quality rules such as null checks, schema validation, and integrity checks• Use DataGaps (preferred) or similar tools for data quality monitoring• Collaborate with data engineers, architects, and product teams to ensure quality• Validate ETL workflows, scheduling, and failure handling mechanisms• Execute SQL and Python-based validations for batch and real-time data• Integrate automated data tests into CI/CD pipelines• Track and report defects using Zephyr and Jira• Ensure compliance with data governance and regulatory standards• Mentor junior engineers and contribute to best practices and frameworksRequired Skills:• Strong experience in data pipeline validation, ETL testing, and data quality engineering• Proficiency in Python, PySpark, SQL, and Pytest• Experience with Selenium and DataGaps frameworks• Knowledge of CI/CD tools (GitHub Actions) and version control (Git)• Experience with AWS (Glue, Iceberg) and lakehouse architectures• Familiarity with test data management techniques• Experience with performance testing tools (JMeter)• Experience with Zephyr test management tool• Knowledge of Docker and KubernetesPreferred:• Experience with healthcare or regulated datasets• Experience validating APIs, Tableau reports, and UI• Familiarity with Agile methodologiesEducation:• Bachelor’s degree in Computer Science, Engineering, or related fieldJob Title: Senior Data Quality EngineerExperience: 6–8 YearsJob Description:• Own end-to-end data quality validation across ingestion, transformation, and consumption layers• Design and maintain ETL automation frameworks using Python and Pytest• Perform source-to-target validation, reconciliation, and anomaly detection• Validate cloud-based data architectures (data lakes, warehouses, streaming platforms)• Implement data quality rules such as null checks, schema validation, and integrity checks• Use DataGaps (preferred) or similar tools for data quality monitoring• Collaborate with data engineers, architects, and product teams to ensure quality• Validate ETL workflows, scheduling, and failure handling mechanisms• Execute SQL and Python-based validations for batch and real-time data• Integrate automated data tests into CI/CD pipelines• Track and report defects using Zephyr and Jira• Ensure compliance with data governance and regulatory standards• Mentor junior engineers and contribute to best practices and frameworksRequired Skills:• Strong experience in data pipeline validation, ETL testing, and data quality engineering• Proficiency in Python, PySpark, SQL, and Pytest• Experience with Selenium and DataGaps frameworks• Knowledge of CI/CD tools (GitHub Actions) and version control (Git)• Experience with AWS (Glue, Iceberg) and lakehouse architectures• Familiarity with test data management techniques• Experience with performance testing tools (JMeter)• Experience with Zephyr test management tool• Knowledge of Docker and KubernetesPreferred:• Experience with healthcare or regulated datasets• Experience validating APIs, Tableau reports, and UI• Familiarity with Agile methodologiesEducation:• Bachelor’s degree in Computer Science, Engineering, or related fieldJob Title: Senior Data Quality EngineerExperience: 6–8 YearsJob Description:• Own end-to-end data quality validation across ingestion, transformation, and consumption layers• Design and maintain ETL automation frameworks using Python and Pytest• Perform source-to-target validation, reconciliation, and anomaly detection• Validate cloud-based data architectures (data lakes, warehouses, streaming platforms)• Implement data quality rules such as null checks, schema validation, and integrity checks• Use DataGaps (preferred) or similar tools for data quality monitoring• Collaborate with data engineers, architects, and product teams to ensure quality• Validate ETL workflows, scheduling, and failure handling mechanisms• Execute SQL and Python-based validations for batch and real-time data• Integrate automated data tests into CI/CD pipelines• Track and report defects using Zephyr and Jira• Ensure compliance with data governance and regulatory standards• Mentor junior engineers and contribute to best practices and frameworksRequired Skills:• Strong experience in data pipeline validation, ETL testing, and data quality engineering• Proficiency in Python, PySpark, SQL, and Pytest• Experience with Selenium and DataGaps frameworks• Knowledge of CI/CD tools (GitHub Actions) and version control (Git)• Experience with AWS (Glue, Iceberg) and lakehouse architectures• Familiarity with test data management techniques• Experience with performance testing tools (JMeter)• Experience with Zephyr test management tool• Knowledge of Docker and KubernetesPreferred:• Experience with healthcare or regulated datasets• Experience validating APIs, Tableau reports, and UI• Familiarity with Agile methodologiesEducation:• Bachelor’s degree in Computer Science, Engineering, or related fieldJob Title: Senior Data Quality EngineerExperience: 6–8 YearsJob Description:• Own end-to-end data quality validation across ingestion, transformation, and consumption layers• Design and maintain ETL automation frameworks using Python and Pytest• Perform source-to-target validation, reconciliation, and anomaly detection• Validate cloud-based data architectures (data lakes, warehouses, streaming platforms)• Implement data quality rules such as null checks, schema validation, and integrity checks• Use DataGaps (preferred) or similar tools for data quality monitoring• Collaborate with data engineers, architects, and product teams to ensure quality• Validate ETL workflows, scheduling, and failure handling mechanisms• Execute SQL and Python-based validations for batch and real-time data• Integrate automated data tests into CI/CD pipelines• Track and report defects using Zephyr and Jira• Ensure compliance with data governance and regulatory standards• Mentor junior engineers and contribute to best practices and frameworksRequired Skills:• Strong experience in data pipeline validation, ETL testing, and data quality engineering• Proficiency in Python, PySpark, SQL, and Pytest• Experience with Selenium and DataGaps frameworks• Knowledge of CI/CD tools (GitHub Actions) and version control (Git)• Experience with AWS (Glue, Iceberg) and lakehouse architectures• Familiarity with test data management techniques• Experience with performance testing tools (JMeter)• Experience with Zephyr test management tool• Knowledge of Docker and KubernetesPreferred:• Experience with healthcare or regulated datasets• Experience validating APIs, Tableau reports, and UI• Familiarity with Agile methodologiesEducation:• Bachelor’s degree in Computer Science, Engineering, or related fieldJob Title: Senior Data Quality EngineerExperience: 6–8 YearsJob Description:• Own end-to-end data quality validation across ingestion, transformation, and consumption layers• Design and maintain ETL automation frameworks using Python and Pytest• Perform source-to-target validation, reconciliation, and anomaly detection• Validate cloud-based data architectures (data lakes, warehouses, streaming platforms)• Implement data quality rules such as null checks, schema validation, and integrity checks• Use DataGaps (preferred) or similar tools for data quality monitoring• Collaborate with data engineers, architects, and product teams to ensure quality• Validate ETL workflows, scheduling, and failure handling mechanisms• Execute SQL and Python-based validations for batch and real-time data• Integrate automated data tests into CI/CD pipelines• Track and report defects using Zephyr and Jira• Ensure compliance with data governance and regulatory standards• Mentor junior engineers and contribute to best practices and frameworksRequired Skills:• Strong experience in data pipeline validation, ETL testing, and data quality engineering• Proficiency in Python, PySpark, SQL, and Pytest• Experience with Selenium and DataGaps frameworks• Knowledge of CI/CD tools (GitHub Actions) and version control (Git)• Experience with AWS (Glue, Iceberg) and lakehouse architectures• Familiarity with test data management techniques• Experience with performance testing tools (JMeter)• Experience with Zephyr test management tool• Knowledge of Docker and KubernetesPreferred:• Experience with healthcare or regulated datasets• Experience validating APIs, Tableau reports, and UI• Familiarity with Agile methodologiesEducation:• Bachelor’s degree in Computer Science, Engineering, or related fieldJob Title: Senior Data Quality EngineerExperience: 6–8 YearsJob Description:• Own end-to-end data quality validation across ingestion, transformation, and consumption layers• Design and maintain ETL automation frameworks using Python and Pytest• Perform source-to-target validation, reconciliation, and anomaly detection• Validate cloud-based data architectures (data lakes, warehouses, streaming platforms)• Implement data quality rules such as null checks, schema validation, and integrity checks• Use DataGaps (preferred) or similar tools for data quality monitoring• Collaborate with data engineers, architects, and product teams to ensure quality• Validate ETL workflows, scheduling, and failure handling mechanisms• Execute SQL and Python-based validations for batch and real-time data• Integrate automated data tests into CI/CD pipelines• Track and report defects using Zephyr and Jira• Ensure compliance with data governance and regulatory standards• Mentor junior engineers and contribute to best practices and frameworksRequired Skills:• Strong experience in data pipeline validation, ETL testing, and data quality engineering• Proficiency in Python, PySpark, SQL, and Pytest• Experience with Selenium and DataGaps frameworks• Knowledge of CI/CD tools (GitHub Actions) and version control (Git)• Experience with AWS (Glue, Iceberg) and lakehouse architectures• Familiarity with test data management techniques• Experience with performance testing tools (JMeter)• Experience with Zephyr test management tool• Knowledge of Docker and KubernetesPreferred:• Experience with healthcare or regulated datasets• Experience validating APIs, Tableau reports, and UI• Familiarity with Agile methodologiesEducation:• Bachelor’s degree in Computer Science, Engineering, or related fieldJob Title: Senior Data Quality EngineerExperience: 6–8 YearsJob Description:• Own end-to-end data quality validation across ingestion, transformation, and consumption layers• Design and maintain ETL automation frameworks using Python and Pytest• Perform source-to-target validation, reconciliation, and anomaly detection• Validate cloud-based data architectures (data lakes, warehouses, streaming platforms)• Implement data quality rules such as null checks, schema validation, and integrity checks• Use DataGaps (preferred) or similar tools for data quality monitoring• Collaborate with data engineers, architects, and product teams to ensure quality• Validate ETL workflows, scheduling, and failure handling mechanisms• Execute SQL and Python-based validations for batch and real-time data• Integrate automated data tests into CI/CD pipelines• Track and report defects using Zephyr and Jira• Ensure compliance with data governance and regulatory standards• Mentor junior engineers and contribute to best practices and frameworksRequired Skills:• Strong experience in data pipeline validation, ETL testing, and data quality engineering• Proficiency in Python, PySpark, SQL, and Pytest• Experience with Selenium and DataGaps frameworks• Knowledge of CI/CD tools (GitHub Actions) and version control (Git)• Experience with AWS (Glue, Iceberg) and lakehouse architectures• Familiarity with test data management techniques• Experience with performance testing tools (JMeter)• Experience with Zephyr test management tool• Knowledge of Docker and KubernetesPreferred:• Experience with healthcare or regulated datasets• Experience validating APIs, Tableau reports, and UI• Familiarity with Agile methodologiesEducation:• Bachelor’s degree in Computer Science, Engineering, or related fieldJob Title: Senior Data Quality EngineerExperience: 6–8 YearsJob Description:• Own end-to-end data quality validation across ingestion, transformation, and consumption layers• Design and maintain ETL automation frameworks using Python and Pytest• Perform source-to-target validation, reconciliation, and anomaly detection• Validate cloud-based data architectures (data lakes, warehouses, streaming platforms)• Implement data quality rules such as null checks, schema validation, and integrity checks• Use DataGaps (preferred) or similar tools for data quality monitoring• Collaborate with data engineers, architects, and product teams to ensure quality• Validate ETL workflows, scheduling, and failure handling mechanisms• Execute SQL and Python-based validations for batch and real-time data• Integrate automated data tests into CI/CD pipelines• Track and report defects using Zephyr and Jira• Ensure compliance with data governance and regulatory standards• Mentor junior engineers and contribute to best practices and frameworksRequired Skills:• Strong experience in data pipeline validation, ETL testing, and data quality engineering• Proficiency in Python, PySpark, SQL, and Pytest• Experience with Selenium and DataGaps frameworks• Knowledge of CI/CD tools (GitHub Actions) and version control (Git)• Experience with AWS (Glue, Iceberg) and lakehouse architectures• Familiarity with test data management techniques• Experience with performance testing tools (JMeter)• Experience with Zephyr test management tool• Knowledge of Docker and KubernetesPreferred:• Experience with healthcare or regulated datasets• Experience validating APIs, Tableau reports, and UI• Familiarity with Agile methodologiesEducation:• Bachelor’s degree in Computer Science, Engineering, or related fieldJob Title: Senior Data Quality EngineerExperience: 6–8 YearsJob Description:• Own end-to-end data quality validation across ingestion, transformation, and consumption layers• Design and maintain ETL automation frameworks using Python and Pytest• Perform source-to-target validation, reconciliation, and anomaly detection• Validate cloud-based data architectures (data lakes, warehouses, streaming platforms)• Implement data quality rules such as null checks, schema validation, and integrity checks• Use DataGaps (preferred) or similar tools for data quality monitoring• Collaborate with data engineers, architects, and product teams to ensure quality• Validate ETL workflows, scheduling, and failure handling mechanisms• Execute SQL and Python-based validations for batch and real-time data• Integrate automated data tests into CI/CD pipelines• Track and report defects using Zephyr and Jira• Ensure compliance with data governance and regulatory standards• Mentor junior engineers and contribute to best practices and frameworksRequired Skills:• Strong experience in data pipeline validation, ETL testing, and data quality engineering• Proficiency in Python, PySpark, SQL, and Pytest• Experience with Selenium and DataGaps frameworks• Knowledge of CI/CD tools (GitHub Actions) and version control (Git)• Experience with AWS (Glue, Iceberg) and lakehouse architectures• Familiarity with test data management techniques• Experience with performance testing tools (JMeter)• Experience with Zephyr test management tool• Knowledge of Docker and KubernetesPreferred:• Experience with healthcare or regulated datasets• Experience validating APIs, Tableau reports, and UI• Familiarity with Agile methodologiesEducation:• Bachelor’s degree in Computer Science, Engineering, or related field

Job Title: Senior Data Quality Engineer

Job Title:

Experience: 6–8 Years

Experience:Job Description:Job Description:• Own end-to-end data quality validation across ingestion, transformation, and consumption layersdata quality validation• Design and maintain ETL automation frameworks using Python and PytestETL automation frameworks• Perform source-to-target validation, reconciliation, and anomaly detectionsource-to-target validation, reconciliation, and anomaly detection• Validate cloud-based data architectures (data lakes, warehouses, streaming platforms)cloud-based data architectures• Implement data quality rules such as null checks, schema validation, and integrity checksdata quality rules• Use DataGaps (preferred) or similar tools for data quality monitoringDataGaps (preferred)• Collaborate with data engineers, architects, and product teams to ensure qualitydata engineers, architects, and product teams• Validate ETL workflows, scheduling, and failure handling mechanismsETL workflows, scheduling, and failure handling mechanisms• Execute SQL and Python-based validations for batch and real-time dataSQL and Python-based validations• Integrate automated data tests into CI/CD pipelinesautomated data tests into CI/CD pipelines• Track and report defects using Zephyr and JiraZephyr and Jira• Ensure compliance with data governance and regulatory standardsdata governance and regulatory standards• Mentor junior engineers and contribute to best practices and frameworksbest practices and frameworksRequired Skills:Required Skills:• Strong experience in data pipeline validation, ETL testing, and data quality engineeringdata pipeline validation, ETL testing, and data quality engineering• Proficiency in Python, PySpark, SQL, and PytestPython, PySpark, SQL, and Pytest• Experience with Selenium and DataGaps frameworksSelenium and DataGaps frameworks• Knowledge of CI/CD tools (GitHub Actions) and version control (Git)CI/CD tools (GitHub Actions) and version control (Git)• Experience with AWS (Glue, Iceberg) and lakehouse architecturesAWS (Glue, Iceberg) and lakehouse architectures• Familiarity with test data management techniquestest data management techniques• Experience with performance testing tools (JMeter)performance testing tools (JMeter)• Experience with Zephyr test management toolZephyr test management tool• Knowledge of Docker and KubernetesDocker and KubernetesPreferred:Preferred:• Experience with healthcare or regulated datasetshealthcare or regulated datasets• Experience validating APIs, Tableau reports, and UIAPIs, Tableau reports, and UI• Familiarity with Agile methodologiesAgile methodologiesEducation:Education:• Bachelor’s degree in Computer Science, Engineering, or related fieldComputer Science, Engineering, or related fieldAt Indium, diversity, equity, and inclusion (DEI) are core values. We promote DEI through a dedicated council, expert sessions, and tailored training, fostering an inclusive workplace. Our initiatives, like the WE@IN women empowerment program and DEI calendar, create a culture of respect and belonging. Recognized with the Human Capital Award, we're committed to an environment where everyone thrives. Join us in building a diverse, innovative workplace.Indium do not solicit or accept any form of fees or payment at any stage of the hiring process.At Indium, diversity, equity, and inclusion (DEI) are core values. We promote DEI through a dedicated council, expert sessions, and tailored training, fostering an inclusive workplace. Our initiatives, like the WE@IN women empowerment program and DEI calendar, create a culture of respect and belonging. Recognized with the Human Capital Award, we're committed to an environment where everyone thrives. Join us in building a diverse, innovative workplace.At Indium, diversity, equity, and inclusion (DEI) are core values. We promote DEI through a dedicated council, expert sessions, and tailored training, fostering an inclusive workplace. Our initiatives, like the WE@IN women empowerment program and DEI calendar, create a culture of respect and belonging. Recognized with the Human Capital Award, we're committed to an environment where everyone thrives. Join us in building a diverse, innovative workplace.At Indium, diversity, equity, and inclusion (DEI) are core values. We promote DEI through a dedicated council, expert sessions, and tailored training, fostering an inclusive workplace. Our initiatives, like the WE@IN women empowerment program and DEI calendar, create a culture of respect and belonging. Recognized with the Human Capital Award, we're committed to an environment where everyone thrives. 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