Data Lead

Lead : 6-8 years

• Focus on production cost for the techniques and features

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  Mentoring the team on benchmarking costs, performance KPI’s

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  Guarding the focus on the team towards objectives

  •Advanced proficiency in Python and/or Scala for data engineering tasks. •Proficiency in PySpark and Scala Spark for distributed data processing, with hands-on experience in Azure Databricks. •Expertise in Azure Databricks for data engineering, including Delta Lake, MLflow, and cluster management. •Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and their big data and data warehousing services (e.g., Azure Data Factory, AWS Redshift). •Expertise in data warehousing platforms such as Snowflake, Azure Synapse Analytics, or Redshift, including schema design, ETL/ELT processes, and query optimization. •Experience with Hadoop ecosystem (HDFS, Hive, HBase, etc.), Apache Airflow for workflow orchestration and scheduling. •Advanced knowledge of SQL for data warehousing and analytics, with experience in NoSQL databases (e.g., MongoDB) as a plus. •Experience with version control systems (e.g., Git) and CI/CD pipelines. •Familiarity with Java or other programming languages is a plus.