Posted 12 June, 2026
Go Application Data Engineer
NR Consulting
Bangalore,Karnataka,560100
Full Time
Reference: 365_463738_24-03778
Job Title: Application Data Engineer
Location: Remote
Job Description:
Tech Stack:
ETL Tools: Strong experience with Apache Airflow or similar ETL tools
Databases: Advanced knowledge of database management systems,
expertise in SQL databases like PostgresSQL and familiarity of NOSQL
databases
Data Warehousing: Experience with data warehousing solutions
Data Modelling: Proficient in data modelling, both logical and physical
Indexing: Knowledge of different types of indexes and use cases
Programming Languages: Proficiency in Golang is a plus
Version Control: Proficiency in Git
Cloud Services: Experience with Cloud service provider such as Azure,
AWS or GCP
Monitoring Tools: Familiarity with monitoring tool is a plus
Other Tools: Understanding of messaging queues and infrastructure as
code tools is desirable.
Key Responsibilities:
Design, build and optimize data extraction, transformation and loading
processes using ETL tools.
Implement complex data flows that ensure data accuracy and reliability.
Maintain and enhance data warehouse and database systems
Develop and manage robust, scalable data models to support data
science and analytics.
Collaborate closely with backend engineers, and business stakeholders to
support data-driven decision making.
Ensure data integrity and consistency through a comprehensive
understanding of ACID properties and transaction management.
Implement and maintaining indexing strategies to optimize database
performance
Conduct data inspections and audits to ensure high data quality and
troubleshoot data-related issues.
Qualifications:
Bachelor's degree in Computer Science (or related field) or equivalent
practical experience
Strong understanding of ETL processes and orchestration tools like
Apache Airflow
In-depth knowledge of SQL and understanding of NoSQL databases.
Solid grasp of database design, including normalization and
denormalization
Experience with cloud services and infrastructure as a code
Commitment with continuous learning and improvement in data
engineering practice
Location: Remote
Job Description:
Tech Stack:
ETL Tools: Strong experience with Apache Airflow or similar ETL tools
Databases: Advanced knowledge of database management systems,
expertise in SQL databases like PostgresSQL and familiarity of NOSQL
databases
Data Warehousing: Experience with data warehousing solutions
Data Modelling: Proficient in data modelling, both logical and physical
Indexing: Knowledge of different types of indexes and use cases
Programming Languages: Proficiency in Golang is a plus
Version Control: Proficiency in Git
Cloud Services: Experience with Cloud service provider such as Azure,
AWS or GCP
Monitoring Tools: Familiarity with monitoring tool is a plus
Other Tools: Understanding of messaging queues and infrastructure as
code tools is desirable.
Key Responsibilities:
Design, build and optimize data extraction, transformation and loading
processes using ETL tools.
Implement complex data flows that ensure data accuracy and reliability.
Maintain and enhance data warehouse and database systems
Develop and manage robust, scalable data models to support data
science and analytics.
Collaborate closely with backend engineers, and business stakeholders to
support data-driven decision making.
Ensure data integrity and consistency through a comprehensive
understanding of ACID properties and transaction management.
Implement and maintaining indexing strategies to optimize database
performance
Conduct data inspections and audits to ensure high data quality and
troubleshoot data-related issues.
Qualifications:
Bachelor's degree in Computer Science (or related field) or equivalent
practical experience
Strong understanding of ETL processes and orchestration tools like
Apache Airflow
In-depth knowledge of SQL and understanding of NoSQL databases.
Solid grasp of database design, including normalization and
denormalization
Experience with cloud services and infrastructure as a code
Commitment with continuous learning and improvement in data
engineering practice
