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Mid-level generalist data engineer in metro with hybrid work attracts many qualified applicants.
Core data engineering skills transfer across industries despite optional healthcare preference.
Explicit 3–5 years plus specific data platform, ETL, and SQL requirements make filters strict.
Design, develop, maintain, and support scalable ETL/ELT data pipelines for enterprise data processing and analytics.
Perform data ingestion, transformation, integration, and validation while ensuring data quality and pipeline performance.
Collaborate with global teams to support investigative analytics (e.g., fraud, waste, abuse) and enhance data models and workflows across production and non-production environments.
3–5 years of experience in Data Engineering or related roles involving enterprise-scale data pipelines.
Bachelor’s degree in Computer Science, IT, Engineering, Data Science, or related field.
Strong SQL skills including complex queries, optimization, transformation, and validation.
Experience with modern cloud data platforms (e.g., Snowflake), ETL/ELT practices, and hybrid remote work location in Noida.
Experienced in supporting investigative analytics use cases like fraud, waste, and abuse, preferably in the healthcare domain.
Proficient in cloud data platforms, distributed processing, data validation, and pipeline orchestration tools (e.g., Airflow, Azure Data Factory).
Comfortable working in offshore delivery or managed services environments with a production support mindset and collaborative style.