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Job Title: Data Engineer (Analyst)

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Experience: 2.5 to 5 Years

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Location: PAN India (Remote/In office as applicable)

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About the Role:

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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.

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Key Responsibilities:

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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)

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Required Skills:

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Strong SQL skills (CTEs, window functions, joins, aggregations, optimization)
Robust Python skills for transformations and automation
Hands-on experience with at least one cloud platform: AWS / Azure / GCP
Experience with a contemporary 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)

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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

Apply on Kit Job: kitjob.in/job/43wnx8

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