Senior Data Engineer

DATA ENGINEER (Databricks & Azure)

Experience : 3 - 8 Years

Location : Noida, Banglore, Pune, Gurugram, Hyderabad, Jaipur

Overview:

As a Data Engineer you will work with multiple teams to deliver solutions on the Azure Cloud using core cloud data warehouse tools (Azure Data Factory, Azure Databricks, Azure Synapse Analytics and other Big Data related technologies). In addition to building the next generation of application data platforms (not infrastructure) and/or improving recent implementations. Experience in Databricks and Spark on other cloud platforms like AWS and GCP is also relevant.

Responsibilities:

Defines, designs, develops and test software components/applications using Microsoft Azure- (Azure Databricks, Azure Data Factory, Azure Data Lake Storage, Logic Apps, Azure SQL database, Azure Key Vaults, ADLS)

Strong SQL skills with experience.

Experience handling Structured and unstructured datasets.

Experience in Data Modeling and Advanced SQL techniques

Experience implementing Azure Data Factory, Airflow, AWS Glue or any other data orchestration tool using latest technologies and techniques.

Good exposure in Application Development.

The candidate should work independently with minimal supervision.

Must Have:

Hands on experience with distributed computing framework like DataBricks, Spark- Ecosystem (Spark Core, PySpark, Spark Streaming, SparkSQL)

Willing to work with product teams to best optimize product features/functions.

Experience on Batch workloads and real time streaming with high volume data frequency

Performance optimization on Spark workloads

Environment setup, user management, Authentication and cluster management on Databricks

Professional curiosity and the ability to enable yourself in new technologies and tasks.

Good understanding of SQL and a good grasp of relational and analytical database management theory and practice.

Good To Have:

Hands on experience with distributed computing framework like DataBricks.

Experience with Databricks migration from On-premise to Cloud OR Cloud to Cloud

Migration of ETL workloads from Apache Spark implementations to Databricks

Experience on Databricks ML will be a plus

Migration from Spark 2.0 to Spark 3.5

Certifications :

Databricks Solution Architect Essentials badge

Databricks Developer Essentials

Apache Spark Programming with Databricks

Data Engineering with Databricks

Lakehouse with Delta Lake Deep Dive

Fundamentals of Unified Data Analytics with Databricks

Key Skills:

Python, SQL and Pyspark

Big Data Ecosystem (Hadoop, Hive, Sqoop, HDFS, Hbase)

Spark Ecosystem (Spark Core, Spark Streaming, Spark SQL) / Databricks

Azure (ADF, ADB, Logic Apps, Azure SQL database, Azure Key Vaults, ADLS, Synapse)

AWS (Lambda,AWS Glue, S3, Redshift)

Data Modelling, ETL Methodology.

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