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