Data Engineer (Analyst)

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Canopus Infosystems

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Job Description

Job Title: Data Engineer (Analyst) 

Experience: 2.5 to 5 Years 

Location: PAN India (Remote/On-site as applicable) 


About the Role: 

We are looking for Data Engineer (Analyst) to build and maintain reliable data pipelines and analytics-ready datasets that power BI reporting, product insights, and business decision-making. You’ll work across multiple data sources, model clean reporting layers, and ensure data quality end-to-end. 


Key Responsibilities: 

  • Build and maintain scalable ETL/ELT pipelines (batch and incremental) using SQL + Python 
  • Integrate data from databases, APIs, SaaS tools, event data, and flat files 
  • Design analytics-ready data models (star schema/marts) for self-serve reporting 
  • Create and optimize transformations in a cloud warehouse/lakehouse (e.g., Snowflake, BigQuery, Redshift, Synapse, Databricks
  • Partner with stakeholders to define KPIs, metric logic, and reporting requirements 
  • Maintain dashboards and reporting outputs in tools like Power BI, Tableau, Looker, or Sigma 
  • Implement data quality checks, monitoring, alerts, and documentation to keep datasets trusted 
  • Tune performance and cost (incremental loads, partitioning, query optimization, file formats) 


Required Skills: 

  • Strong SQL skills (CTEs, window functions, joins, aggregations, optimization) 
  • Strong Python skills for transformations and automation 
  • Hands-on experience with at least one cloud platform: AWS / Azure / GCP 
  • Experience with a modern data warehouse/lakehouse (Snowflake/BigQuery/Redshift/Synapse/Databricks) 
  • Solid understanding of ETL/ELT patterns (incremental loads, retries, idempotency, basic CDC) 
  • Comfort with data modeling for analytics and BI reporting 
  • Experience building stakeholder-friendly reporting in a BI tool (Power BI/Tableau/Looker/Sigma) 

 

Nice to have:

  • Orchestration tools: Airflow, dbt, Dagster, Prefect, ADF, Glue, etc. 
  • Streaming/event data: Kafka, Kinesis, Pub/Sub 
  • Monitoring/logging: CloudWatch, Azure Monitor, GCP Monitoring, Datadog 
  • CI/CD + Git-based workflows for data pipelines 



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