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Role overview

This position sits within Advisory, focused on Data and Analytics. The role is centered on helping organizations turn data into practical business insight, improve decision-making, and support business growth through modern analytics, data management, and data assurance capabilities.

The work involves building agile reporting and analytics solutions that make it easier for clients to interpret their data, answer key business questions, and uncover opportunities through interactive dashboards and business intelligence tools.

Line of service

Advisory

Industry sector

Not applicable

Specialism

SAP

Management level

Senior Associate

Key responsibilities

Build, refine, and support scalable data pipelines and ETL workflows using PySpark or Scala to move both structured and unstructured data from multiple sources.
Set up and manage data ingestion, transformation, and storage solutions on Azure using services such as Azure Databricks, Azure Data Lake Storage, and Azure Synapse Analytics.
Create and maintain data structures, schemas, and metadata that enable efficient access, better query speed, and stronger analytics support.
Track pipeline health, resolve operational issues, and improve processing workflows so they remain reliable, scalable, and cost-efficient.
Apply security controls and compliance practices to safeguard sensitive data and meet regulatory requirements.

Requirements

Solid background as a Data Engineer with direct experience in designing and optimizing data pipelines using PySpark, Scala, and Apache Spark.
Practical exposure to Azure cloud environments and related services, including Azure Databricks, Azure Data Lake Storage, Azure Synapse Analytics, and Azure SQL Database.
Strong coding ability in Python and Scala, along with working knowledge of software engineering, version control, and CI/CD methods.
Understanding of data warehousing, dimensional modeling, and relational databases such as SQL Server, PostgreSQL, and MySQL.
Exposure to big data ecosystems and tools such as Hadoop, Hive, and HBase will be considered an advantage.
Mandatory skill set: PySpark and Azure.
Experience required: 4 to 8 years.
Qualification required: B.E., B.Tech., or MBA.
Desired languages were not specified.
Travel requirements were not specified.
Work visa sponsorship availability was not specified.
Government clearance requirement was not specified.
Job posting end date was not specified.

Additional information

The role is part of a career path that combines data, business intelligence, and analytics to help clients use smarter insights, make stronger decisions, and adapt to changing technology and customer needs.