As part of CIO/CDO org, leading a team of eight talented data engineers. The team is responsible for creating data products to support internal functions like Sales, Finance, IT and security.#Create Snowflake@Snowflake use cases to solve business problems#Partner with Sales org to demonstrate these use cases to prospects, customers and developer community#Spoke at various Snowflake conferences like Data Cloud World Tour, BUILD, DataWho?#Designed and conducted internal workshop called "AI for Everyone"

As a Data Engineering Manager I along with my team is responsible for building and maintaining Data Platform for Siemens Energy business. Leveraging the power of Data we are optimizing power plant operations by creating suit of Predictive and Prescriptive Data Products.Our Data platform also serves internal Machine Learning and Data Science community to take on various use cases. Also exploring opportunities to provision Data-as-a-Service (Daas) platform.Few Responsibilities and Accomplishments illustrated below # Setup Data team with purpose to take on Siemens Energy Digitalization journey # Define and track OKR for individual and team level# Create career roadmap for individual team members in accordance with there aspiration and organizational goals# Worked with Senior Data Engineers to come up with Data Platform architecture # Evaluated Cloud Data Platform, benchmarked Redshift and Snowflake # Engage with various stakeholders like Business, Apps, Data Scientists and Prioritize requirements # Built initial Data Pipelines and Data Monitoring framework # Built real time data pipeline to process sensors data and generate real time analytics.# Setup Data Governance framework like cataloging, object tagging, Dynamic Data Masking # Introduced DataBaseTools (dbt) framework within team and working on POC to examine the benefits. Tech Stack#AWS #Snowfalke #Python #Tableau #Airflow #CICD #Spark #kafka #kinesis #SQL

As a Lead Data Engineer was responsible for successful implementation of Data Warehouse in life science domain. Using data analytics implemented various use cases to increase Genentech drug sale in US market, along with mandatory compliances reporting. Responsibilities # Designed Data Model and ETL framework # Mentor team members to start implementation of ETL workloads and Dashboards # Code review and quality check before Production go live # Designed Master Data Management strategy and Customer 360 # Key interface between Genentech Data Head and Data Engineers at Saama# Negotiate and priorities requirements coming from clientTech Stack:#AWS #Snowflake #Salesforce #Tableau #Python #Informatica #MDM #Oracle

As a Senior Data Engineer worked closely with Data Engineering Manager and Frontend Netbanking team to understand there Data requirements. Built ETL Data pipelines to onboard Customer and Account data from various internal and External data sources, cleanse and enrich datasets and made it available to be consumed by frontend Netbanking application.Responsibilities:# Socialise with different line of Business within the bank to understand their requirements. # Analyzing source data, generate data quality matrix# Data profiling and mapping with respect to data model# Built ETL data pipelines to onboard various datasets# Configure MDM stage, load, Match merge, survivorship rules, relationships, hierarchy and other MDM supporting objects# Integrate third party data source (DaaS) and address cleansing engine to enrich customer master data# Unit testing and release management # Create views and enhance web services for downstream data consumers# Educate MDM data consumers about process and rules implemented with MDM# Provide training to L1 production support teamTech Stack:#Oracle #Informatica #MDM #WebServices #DQAnalyser #Jboss #ControlM

Responsibilities: # Worked with data architect to come up with conceptual and physical data model# Installed Informatica MDM hub and cleanse servers in clustered mode using JBoss as application server# Installed other Informatica tools like Power Center, IDQ and configured repositories in across all the development and test environments# Streamlined deployment process by implementing continuous integration tools.# Collaborated with product management to design, build and test systems.# Strengthened developmental methodologies by introducing a code quality document.# Developed shared central vocabulary data sets for internal applications.# Drafted comprehensive reports to document bugs and design flaws.# Debugged and modified software components.# Developed code fixes and enhancements for inclusion in future code releases and patches.

# Created fact and dimension tables for data warehouse project for one of the European bank# Design and developed ETL packages using Microsoft SSIS# Developed SQL stored procedure to extract data from various source systems# Developed SSRS and other Ad hoc reports # Integrated live feeds for stock and Forex rates with core banking system using IBM WTX middle ware solution # Implemented Basel II and Anti money laundering rules in core banking application