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Mid-level generalist Data Engineer at a strong brand in a metro with broad big-data requirements.
Data engineering skills transfer broadly, but enterprise app integrations and big-data tooling increase domain specificity.
Requires concrete big-data tooling experience and production engineering practices, but no fixed years requirement.
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Build and maintain data ingestion and transformation pipelines for structured and unstructured enterprise data sources including ERP, CRM, finance, HR, and more.
Implement data quality checks, profiling, enrichment, and preparation workflows for AI-ready data assets.
Collaborate with senior data architects, AI/ML engineers, and platform engineers to support AI agent configuration through data labelling and contextualization.
Experience building data pipelines using technologies like Python, SQL, PySpark, Apache Spark, Airflow, dbt, or Dagster.
Working knowledge of structured and unstructured data handling from enterprise systems and APIs.
Familiarity with cloud data platforms such as Databricks, Snowflake, BigQuery, Azure Data Lake, AWS S3, or Google Cloud Storage.
Work Experience Required: Not explicitly mentioned in the JD
Experienced in enterprise-scale data engineering focusing on varied data types including structured, semi-structured, and unstructured data.
Hands-on practitioner comfortable with production-grade engineering practices like CI/CD, unit testing, and Git version control.
Able to implement robust data quality, profiling, and transformation processes to deliver AI-consumable data assets in collaboration with cross-functional teams.