Data Engineer

We are hiring an experienced Data Engineer (4–6 years) to design and implement scalable, reliable, and high-performance data infrastructure. This role requires deep technical expertise in building data pipelines, ETL processes, and cloud-based architectures that empower analytics and advanced decision-making across the organization.

Key Responsibilities

• Data Pipeline Development: Design, build, and maintain scalable ETL/ELT pipelines that ensure timely and accurate data flow from diverse sources.

• Data Quality & Governance: Implement data validation, monitoring, and quality frameworks to ensure accuracy, consistency, and reliability of business-critical datasets.

• Storage & Optimization: Optimize data storage and retrieval across relational databases, warehouses, and big data ecosystems.

• Big Data Engineering: Work with distributed data processing systems such as Hadoop, Spark, and Kafka to handle large-scale data ingestion and transformation.

• Cloud Data Platforms: Leverage AWS (Redshift, EMR), GCP (BigQuery, Dataflow), or Azure (Synapse, Databricks) for data storage, processing, and orchestration.

• Collaboration: Partner with Data Analysts, Data Scientists, and Product Teams to deliver clean, structured, and well-documented datasets for analytics and machine learning use cases.

• Performance & Scalability: Continuously improve pipeline efficiency, cost optimization, and fault tolerance in cloud environments.