Associate

Line of Service

Advisory

Industry/Sector

Not Applicable

Specialism

Data, Analytics & AI

Management Level

Associate

& Summary

A career within Data and Analytics services will provide you with the opportunity to help organisations uncover enterprise insights and drive business results using smarter data analytics. We focus on a collection of organisational technology capabilities, including business intelligence, data management, and data assurance that help our clients drive innovation, growth, and change within their organisations in order to keep up with the changing nature of customers and technology. We make impactful decisions by mixing mind and machine to leverage data, understand and navigate risk, and help our clients gain a competitive edge.
Creating business intelligence from data requires an understanding of the business, the data, and the technology used to store and analyse that data. Using our Rapid Business Intelligence Solutions, data visualisation and integrated reporting dashboards, we can deliver agile, highly interactive reporting and analytics that help our clients to more effectively run their business and understand what business questions can be answered and how to unlock the answers.

• 1. Understanding of data warehousing concepts
• a. Concept of data warehousing, difference with traditional databases, difference with
• data lake
• b. Data Model concepts (basic) , facts and dimension, measures and references,
• normalization and denormalization
• c. Operating a data warehouse fundamentals, indexes, views and materialized views,
• result set caches, external tables, Polybase and data loading methods in data warehouse
• d. Incremental data loading concepts, change data capture, hashing, row level and column
• level security fundamentals
• e. SQL concepts, fundamental and moderate – Different types of joins and aggregations,
• analytical functions, text parsing and null handing functions, data time and typecasting
• functions, grouping logic
• 2. Understanding of ETL /ELT Process
• a. Difference between ETL and ELT and why each of them is relevant and where
• b. Exception handling in an ETL process, checkpoints, and rerun scenarios
• c. Parallelism and looping concepts in ETL
• d. Scheduling and event triggers in ETL
• 3. Proficient in development using Azure Data factory
• a. Types of Integration runtime and where they are necessary and why
• b. Dataset fundamentals, types of sinks and sources, parameterizing dataset elements
• c. Types of mapping in ADF datasets, mapping, metadata capture, selection/drop
• d. Types of activities, metadata capture, copy using loops, pipeline basics and error path
• handling
• e. Authorization and authentication of linked services connecting to different azure
• components from ADF (storage, key vault, databricks, Datawarehouse)
• f. Mapping data flow ETL components, joins, aggregations, analytical functions,
• g. Partitioning, splitting, merging, using wild card characters, conditional formatting of
• data, null handling
• h. Invocation of SQL scripts, webhooks, API calls, Notebooks from ADF activities
• 4. Experience in data analytics / data warehousing projects using various Azure data related services
• (data lake, blob storage, synapse etc.)
• a. Azure Storage account – types of storage, which storage is best practiced for which
• scenarios
• b. Difference between BLOB and ADLS Gen2, Hierarchical namespaces, file system drivers
• c. Databricks fundamentals, how to mount ADLS file systems, authentication and
• authorization methods, access to Key Vaults, utilities, and parameters in Databricks
• d. Logic apps fundamentals, what are events and triggers, different event triggers such as
• blob triggers, HTTP calls, REST calls, email responses connectivity and workflow
• e. Azure Automation account fundamentals, automation basics on libraries and providers,
• PowerShell scripting fundamentals, VM startup and shutdown automation, DWH scaling automation
• f. Synapse Ecosystem knowledge, connectivity between different synapse internal
• components such as Synapse warehouse with Storage, Spark Pool with Storage, ADF and Mapping Data
• flow with Warehouse and storage.
• f. Synapse Secured connectivity concepts – Managed VNET and Self hosted IR in VNET, Private
• endpoints, Service endpoints, firewall whitelisting and IP rules (good to have knowledge)
• Experience (good to have):
• 1. Knowledge in JSON scripts, Azure infrastructure (VMs, networking components etc
• 2. Serverless features in serverless sql pool in synapse ecosystem
• 3. Azure functions and function app development
• 4. Data Quality and MDM concepts, quality rules implementation in Spark/Python, modular
• rulebooks, MDM match merging, survivorship, weightage concepts
• Mandatory Skill Set: ADF, DataLake,ETL
• Preferred Skill Set: ADF, DataLake,ETL
• Year of experience required: 3-9
• Qualifications: Btech/MCA/MBA/Mtech

Education (if blank, degree and/or field of study not specified)

Degrees/Field of Study required:Degrees/Field of Study preferred:

Certifications (if blank, certifications not specified)

Required Skills

Data Lake, ETL Tools

Optional Skills

Desired Languages (If blank, desired languages not specified)

Travel Requirements

Available for Work Visa Sponsorship?

Government Clearance Required?

Job Posting End Date