Data Engineer

Proficiency in managing and manipulating huge datasets in the order of terabytes (TB) is essential.

Expertise in big data technologies like Hadoop, Apache Spark (Scala preferred), Apache Hive, or similar frameworks on the cloud (Google Cloud Platform preferred, AWS, Azure etc.) to build batch data pipelines with strong focus on optimization, SLA adherence and fault tolerance.

Expertise in building idempotent workflows using orchestrators like Automic, Airflow, Luigi etc.

Expertise in writing SQL to analyze, optimize, profile data preferably in BigQuery or SPARK SQL

Strong data modeling skills are necessary for designing a schema that can accommodate the evolution of data sources and facilitate seamless data joins across various datasets

Ability to work directly with stakeholders to understand data requirements and translate that to pipeline development / data solution work.

Strong analytical and problem-solving skills are crucial for identifying and resolving issues that may arise during the data integration and schema evolution process.

Ability to move at rapid pace with quality and start delivering with minimal ramp up time will be crucial to succeed in this initiative.

Effective communication and collaboration skills are necessary for working in a team environment and coordinating efforts between different stakeholders involved in the project.