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adherence.\nCreate and maintain Delta/Iceberg tables and lakehouse data architectures.\nPartner closely with Data Scientists and ML Engineers to productionize feature engineering pipelines.\nImprove performance, scalability, and cost efficiency of Spark and DBT workloads.\nImplement strong data quality, testing, documentation, and monitoring practices.\nParticipate in peer code reviews and contribute to engineering best practices.\nRequired Skills:\nStrong hands-on experience in SQL, DBT, Databricks, PySpark, and Airflow.\nExperience working with distributed data systems and large-scale data processing.\nGood understanding of data modeling, orchestration, and pipeline optimization.\nExperience with Infrastructure-as-Code tools such as Terraform, AWS CDK, or Pulumi.\nFamiliarity with Delta Lake / Iceberg architectures.\nStrong communication and collaboration skills.\nNice to Have:\nExperience with Kinesis or other streaming systems.\nExposure to MLOps or ML/AI teams.\nExperience working with globally distributed engineering teams.\nFamiliarity with AI-assisted engineering tools such as Cursor, Copilot, or Claude Code.\nPlease go through the meeting notes from our client meeting last night, with the hiring managers for the data engineer 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