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Mid-level generalist data engineer in Gurgaon with broad toolset and popular title increases applicant competition.
Requires pharma-specific datasets and life-sciences domain knowledge, limiting cross-industry transferability.
Explicit 4-8 years plus mandatory ETL, warehouse, pipeline and governance tools indicates strict technical filters.
Develop and maintain robust, reusable data querying, transformation, and visualization pipelines to support business analytics.
Integrate multiple data sources into unified visualizations and dashboards, collaborating with UX/UI teams to deliver best-in-class customer solutions.
Engage directly with customers and platform leaders to understand business problems, ensure data governance, and align analytics solutions with enterprise platforms.
4-8 years of experience in data warehousing and data engineering.
Hands-on experience with ETL tools such as Azure Data Factory (ADF), Databricks, and Informatica.
Proficient with data pipeline and workflow management tools like Azkaban, Luigi, or Airflow.
Experience with data warehouses: SQL/NoSQL databases, Amazon Redshift, Snowflake, Apache Hive, HDFS.
Strong understanding of pharmaceutical industry datasets (e.g., LAAD, DDD, XPO) and commercial analytics in life sciences.
Proven ability to design enterprise data warehouse solutions from scratch, indicating strategic and technical leadership.
Experienced in applying data governance and quality management frameworks within analytics implementations.