Abhinav Aditya

• Client : Virgin Media O2
  Domain : Telecommunication
• Successfully delivered project where we had to re-engineer on premise Ab-Initio solution that was used by the business for Smartmeter performance reporting.
• Involved in all aspects of project delivery, from making decisions for architecture of strategic ETL process, created low level design of ETL process, implementation using PySpark and deployment in production using Azure DevOps in an Agile methodology.
• Worked on multiple performance improvement and optimizations to help the client have hard and soft dollar savings in Opex cost.
• Lead globally distributed development team in multi vendor setup, facilitating design discussions with Solution Designers, Architects, legacy system SMEs', Functional team members and PMs'
• Managed and guided team with design, development and end to end delivery of project. Actively working on Azure Synapse using Spark Framework to deliver the solution.
• Experienced in sprint planning, deployment planning, using Azure DevOps user stories, supporting testing team and other external teams during UAT, solution optimization with architects to speed up creation of reports in Data Lake in a medallion architecture.
• Actively worked on gap analysis during project delivery and highlighting risks to stakeholders. Planned mitigation for all the risks.

Client: DBS Bank

Domain: Retail Banking

Cross Program Analytics : The objective of the project is to develop an analytical platform from Bank's BU datasets. It consists of financial transactions augmented with additional attributes from various channels necessary for transaction screening and fraud detection.

• Project CATA :
• Successfully developed Customer account transaction alert system which contains all transaction alerts on country basis from current account systems enriched with data attributes of in house payment engines. The development was done using PySpark with storing data residing in Hive tables.
• Created data lake on ADLS with the pipelines being created using Airflow. Developed transformation logic for datasets in Datamart using PySpark.
• Created data quality framework based on rules provided by business to determine the quality of data between multiple source systems.
• Worked in Agile environment performing best practices in incorporating multiple data streams within the pipeline.
• Facilitated discussions with business and technical architects to bridge gap between the functional requirement and overall design.
• Developed multiple file parsers as source files were in multiple formats to enrich target entities with rules for business analyst team to create Tableau reports.

Client: Credit Suisse IB

Domain: Investment Bank

Central Data Distribution Services and Analytics:
The objective of the project is to develop a Data Lake for Central Data Distribution mechanism on Hadoop with data analytics on the data quality of the Critical Data Elements, Reconciliations between legacy systems and new systems, Data Usage patterns and Price Predictions in the reference data domain.

• Data Quality:
• Interact with regulators and business owners to determine rules for Critical Data Elements. Develop those rules and transformations using Hive/Hadoop and Spark.
• Generate analytic based on data quality and store it in data lake. Display final data and analysis in tableau reports in daily/weekly/monthly basis.
• Successfully created Data Quality and Reconciliation frameworks to provide critical insights to business in reference data domain.
• Pricing Data Distribution:
• Import near real time data from various sources such as Six Financial Services, Refinitiv, Bloomberg etc using file FTP, Kafka, MQ Subscriber.
• Developed file parser using Spark. Compare prices with various sources and if prices do not match then create pending activity for back office team. Store and maintain segregated data for downstream application.
• Developed Code using Spark-Scala and setup automated jobs in Control-M for data import from various system into Hadoop environment. Developed and published tableau reports on UAT and Production for Consumers and stakeholders.
• Involved in interaction with client and business partners to understand use cases and establish rules for data quality and data reconciliations.
• Actively participated in technical design and architecture of project with architect and stakeholders.
• Provided solution design for creating complex file parsers and actively worked with Business Analysts' to get alignment on Data mappings across systems within organization

Client : InfoGroup – Tractor Supply Co.
Domain : E-Commerce
Master Data Management : The objective of building a Master Data Management for TSC (Tractor Supply Company) to advance its Customer Data Identification (CDI) and Analytics capabilities through implementation of advanced customer identification process.

• Developed code to load data from historical, daily and weekly files. Files were being consumed from AWS S3 into Hadoop MR jobs.
• Job is to enrich data with correct user information which includes updating user information with correct names, address and demographics.
• Custom process includes address data cleansing, customer matching, and enrichment of data. Entire process is automated using Apache's Airflow.
• Developed pipelines using Airflow. Created Hive tables to store data once processed.
• Worked effectively with team in an Agile framework.