Data Engineer

Shift: 2:00 PM – 11:00 PM IST
Job Summary

We are looking for a skilled Data Engineer with 4+ years of hands-on experience in building and maintaining scalable data platforms and ETL/ELT solutions.

The ideal candidate should have strong production experience with Python, SQL, Snowflake, DBT, and Apache Airflow/Prefect, along with exposure to analytics and BI tools such as DOMO. The candidate should be experienced in developing reliable, high-performance data pipelines that support analytics, reporting, and business decision-making.

Key Responsibilities

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  Design, develop, and maintain scalable ETL/ELT pipelines using Python and SQL.

  
  


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  Ingest, transform, validate, and deliver high-quality data from multiple sources.

  
  


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  Build, schedule, monitor, and troubleshoot automated workflows using Apache Airflow or Prefect.

  
  


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  Develop and maintain DBT models for data transformation, testing, documentation, and reusable data models.

  
  


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  Design, implement, and optimize data warehouse solutions using Snowflake and GCP BigQuery.

  
  


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  Perform schema design, query optimization, performance tuning, and cost optimization.

  
  


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  Write complex and optimized SQL queries for data extraction, transformation, reporting, and analytics.

  
  


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  Implement data quality checks, validation frameworks, monitoring, error handling, and pipeline performance improvements.

  
  


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  Collaborate with Data Analysts, Data Scientists, Product Teams, and Business Stakeholders to understand requirements and deliver scalable data solutions.

  
  


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  Follow data engineering best practices related to Git, version control, testing, documentation, governance, and maintainability.

  
  


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  Implement monitoring, logging, and observability practices for production data pipelines.

  
  

Required Skills & Experience

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  4+ years of hands-on experience in Data Engineering.

  
  


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  Strong hands-on experience with Snowflake and DBT — mandatory.

  
  


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  Strong SQL skills, including: Complex queries, Joins, CTEs ,Window functions,  Query optimization, Performance tuning

  
  


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  Recent hands-on production experience with Snowflake, DBT, and SQL.

  
  


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  Strong proficiency in Python.

  
  


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  Strong experience developing DBT models, transformations, tests, and documentation.

  
  


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  Good understanding and hands-on experience in Data Modeling, including dimensional modeling.

  
  


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  Experience with workflow orchestration tools such as Apache Airflow or Prefect.

  
  


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  Experience with DOMO or other Analytics/BI tools.

  
  


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  Proven experience designing, developing, and optimizing ETL/ELT pipelines.

  
  


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  Strong understanding of data quality, troubleshooting, query optimization, and performance tuning.

  
  


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  Good analytical, problem-solving, communication, and collaboration skills.

  
  

Preferred Qualifications

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  Experience with Google Cloud Platform (GCP) and cloud-based data platforms.

  
  


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  Knowledge of CI/CD practices for data engineering projects.

  
  


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  Hands-on experience with Git and version control.

  
  


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  Experience implementing monitoring, logging, and observability for data pipelines.

  
  


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  Experience working in production-scale data warehouse environments.

  
  

Technical SkillsAreaRequired SkillsProgrammingPython, SQLData ModelingDimensional Modeling, Data Warehouse ModelingData WarehousingSnowflake – Strong Hands-on Experience RequiredData TransformationDBT – Strong Hands-on Experience RequiredWorkflow OrchestrationApache Airflow / PrefectAnalytics / BIDOMO or any Analytics/BI ToolCloudGCP / BigQueryCore ExpertiseETL/ELT, Data Quality, Query Optimization, Performance TuningVersion ControlGitMonitoringLogging, Monitoring & ObservabilityCandidate Screening Criteria

Snowflake, DBT, and SQL are core requirements for this role.

Candidates should be able to demonstrate:

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  Strong fundamentals in Snowflake, DBT, and SQL.

  
  


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  Recent, hands-on experience working with these technologies in a production data engineering environment.

  
  


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  Experience designing and optimizing real-world ETL/ELT pipelines.

  
  


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  Strong understanding of data modeling and data warehouse concepts.

  
  


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  Practical experience troubleshooting and optimizing production data pipelines.

  
  

Candidates without recent hands-on production experience in Snowflake and DBT may not be considered.

What We're Looking For

We are looking for someone who can take ownership of data engineering initiatives, build reliable and scalable pipelines, troubleshoot production issues, and work closely with cross-functional teams to deliver high-quality data solutions.