Data Engineer (Analyst)

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