Prashant Mhaske

Data Engineer

Aurangabad, Maharashtra, India1 yr 5 mos experience

Key Highlights

Expert in building scalable ETL pipelines.
Proficient in Azure Data services and Databricks.
Strong focus on data quality and governance.

Stackforce AI infers this person is a Big Data Engineer specializing in Azure and data pipeline optimization.

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Skills

Core Skills

Azure Data FactoryDatabricks

Other Skills

SQLPythonPySparkUnity CatalogAzure SynapseHadoopHiveSynapseMicrosoft AzureApache SparkLinuxApache NiFiSqoop

About

Big Data Engineer | Proficient in PySpark, Spark, Python, SQL, Unity Catalog, Azure Synapse, Databricks, Azure Data Factory, Snowflake , Hadoop, Hive, Kafka, AWS (S3, EC2, EMR, IAM, Athena, Glue) I am a skilled Big Data Engineer with hands-on experience in leveraging big data technologies to develop high-quality data solutions. My expertise includes working with PySpark, Spark, Python, SQL, Unity Catalog, Azure Synapse, Databricks, Azure Data Factory, Hadoop, Hive, Kafka, and AWS (S3, EC2, EMR, IAM, Athena, Glue). I specialize in designing and implementing scalable ETL pipelines, optimizing queries, and managing large-scale structured and unstructured datasets. Hands-on experience working on Medallion Architecture (Bronze, Silver, Gold layers) to build robust data pipelines, ensuring data quality, governance, and performance optimization. Passionate about driving actionable insights through data engineering best practices and delivering high-impact solutions for business needs.

Experience

1 yr 5 mos

Total Experience

1 yr 5 mos

Average Tenure

1 yr 5 mos

Current Experience

Zingmind technologies

Associate Data Engineer

Nov 2024 – Present · 1 yr 5 mos · Indore, Madhya Pradesh, India · On-site

Developed and optimized data pipelines in Azure Data Factory to migrate both historical and incremental (CDC) data from source systems.
Designed and scheduled workflows to ingest data into Azure Data Lake, and orchestrated migration to Delta Lake and Azure Synapse Analytics for structured storage and reporting.
Utilized Databricks Unity Catalog for centralized governance and applied metadata and column-level statistics to optimize data pipelines and query performance.
Conducted thorough data validation between source and target systems using both manual checks and automation scripts to ensure data integrity.
Executed quality assurance (QA) on various data use cases, ensuring business rules and transformation logic are correctly applied.
Deployed and automated custom Python/PySpark scripts in Azure Databricks for scalable data processing and advanced analytics use cases.
Implemented automation for deployment, cluster scheduling, and termination in Databricks, enabling cost-effective, on-demand resource usage.

Azure Data FactoryDatabricksSQLPythonPySparkUnity Catalog+3