Apply proprietary and industry leading methodologies to process and govern data assets for the client in a transaction or value creation opportunity Able to implement frameworks and models covering data strategy components including but not limited to data governance, data quality, data monetization, data consumption and enterprise data management Build and implement cloud / on-prem solutions to help with data driven decision intelligence – this may entail working on mid to high complex ELT (extract, load, transform) techniques leading to modern data warehouse / data mart setup and establishing a business intelligence layer on it Able to handle large data volumes using most latest techniques and data tools Work on buy /sell-side due diligence engagements; and implementing value creation opportunities for analytics application Hands on deliver mid to large size deals and ensure smooth delivery on assigned engagements, products with regions Ability to communicate effectively with cross functional teams across regions Direct client engagement delivery. A strong work initiative, high-energy level, and ability to adapt to new challenges and concepts, including a proactive approach and capacity to perform multiple workstreams and tasks A desire and ability to travel while working within the transaction advisory, financial accounting advisory and mergers and acquisition-oriented environment Assisting clients with building solutions to unlock value using data-driven approaches and business intelligence Ability to work both independently and as part of a team, including robust communication skills Good exposure on financial, accounting, analytical and communication skills, facilitating clear and concise messaging to teammates, clients and other technology personnel Uphold the firm's code of ethics and business conduct.
Minimum of 3-5 years related work experience in Data Engineering / Business Intelligence areas, developing and implementing ‘data as an asset’ service and end to end integrated BI tools to deliver key insights on projects 2+ years of proven experience working on MS Azure (preferred) services – Azure Data Factory, Azure Data Explorer, Storage, Azure Data lake, Synapse Analytics, Azure Analysis services and Databricks Strong working experience using business intelligence tools (PowerBI, Alteryx, Tableau etc) with familiarity of advanced techniques of Geo Spatial analysis and dashboarding through inbuilt or specific tools like Azure Maps, ArcGIS etc Awareness of MS Azure based deployment with foundational knowledge of Azure Cognitive Services to provide an interlock for the Data Science pillar Ability to program using Python, NoSQL, PL/SQL Good understanding of BI practices, analysis, visualization and latest trends Basic awareness on statistical techniques like linear and non-linear regressions / classification and optimization areas Awareness of data orchestration, ingestion, ELT, data design patterns and reporting architecture for both on-prem and cloud (MS Azure preferred) Experienced in Merger & Acquisition domain areas like due diligence, deal structuring, financial reporting, value creation and post-merger phases A Bachelor’s degree in Engineering from tier 1 college (Master’s in Business / domain would be nice to have) Experienced in programming, business intelligence (BI) and enterprise resource systems, including data collection, analysis and presentation: Analytics and data Platforms (AWS, Azure, Snowflake – any one or more of these, preferred Azure) Businesss Intelligence layer (PowerBI, Tableau etc – must have) Programming languages (Python – good to have)
Superior analytical and problem-solving skills Excellent interpersonal and client relationships skills Strong written and verbal communication skills Dedication to teamwork Proficiency in Microsoft Office, with focus on Word and Excel Ability to adapt to ever changing client demands Flexibility to travel, as needed
Desire to grow into a techno-functional consultant role Able to run business performance management techniques including financial reporting, scenario and optimization models Participating in discussions held with Management to determine data analytic needs, data requests and potential impacts on the buy-side/sell-side diligence process, as well as prospective on-going projects