Position Title : Data Engineer

Place of Posting : New Delhi

Experience - 5 to 10 years

As Data Engineer, expectations are as follows :

At Bada Business, we are expanding our data platform capabilities and are looking for a Data Engineer who can designs, builds, and maintains data pipelines and platform components that power analytics, BI, and downstream applications.

The role focuses on scalable ETL/ELT, data warehousing, data quality, and efficient use of cloud-based data infrastructure.

Key Responsibilities :

Data Pipeline Design & Development :

- Build and maintain ETL/ELT pipelines using tools such as Spark, Airflow, Data Factory, or similar.

- Implement both batch and near real-time data flows.

Data Warehousing & Modeling :

- Design and maintain dimensional models for analytics and reporting.

- Work with modern data warehouses (Snowflake, BigQuery, Redshift, Synapse, etc.).

Data Integration & Quality :

- Integrate data from multiple systems (databases, SaaS, APIs, files).

- Implement data validation, profiling, and quality checks.

Performance & Cost Optimization :

- Optimize queries and storage strategies.

- Monitor pipeline performance, data freshness, and cloud costs.

Collaboration & Documentation :

- Work closely with analysts, data scientists, and BI developers.

- Document data lineage, business rules, and technical design.

Required Skills & Experience :

- 3-5 years of hands-on data engineering experience.

- Strong SQL skills and experience with at least one major RDBMS.

- Experience with one or more ETL/ELT and orchestration tools.

- Programming skills in Python or Scala.

- Knowledge of cloud platforms (AWS/Azure/GCP) and their data services.

Preferred Qualifications :

- Cloud data engineering certifications (AWS, Azure, or GCP).

- Experience with streaming tools (Kafka, Kinesis) and big data technologies.

- Experience with dbt or similar transformation tooling.

Success Metrics :

- Pipeline reliability and uptime.

- Data freshness SLAs being consistently met.

- Data quality and reduction of data-related incidents.

- Cost efficiency of data infrastructure.