Data Engineering / Data Science Sr. Manager Resume

PROFESSIONAL PROFILE:

A Data Analytics Manager over 20 years of experience, able to deliver management visions, goals, priorities, design principles, and operating policies in support of the Business goals of the organization.
Provide technical guidance and direction in defining the data architecture strategy for individual projects and the business.
Worked with senior management, technical and client teams both offshore/onsite to determine data requirements and best practices for advance data manipulation, storage and analysis strategies in a data warehouse environment.
Experience in Software Development with knowledge base in Big Data, Analytics, Data Science, Cloud computing and Data warehouse solutions.
Strong leadership, mentoring and interpersonal skills; believe in leading by doing.
Constantly learning and leveraging emerging technologies.

SKILLS/EXPERTISE:

NO SQL: Cassandra, HBase, MongoDB

Big Data Technologies: Hive, Presto, Raptor, Hadoop, Spark,Kinesis

Cloud Computing: AWS (Glue, S3, Data Pipeline, Athena, etc), GCP, Snowflake.

ETL: Data swarm/Airflow, Informatica, Talend, IBM Info Sphere (Data stage), Golden Gate

Language: Python, Scala, Java, JavaScript

Data Science: Python (Pandas/Numpy/Scipy/Sklearn/Tensorflow/PySparks)SAS,R

Database: Hive, Presto, Snowflake, AWS Redshift, Oracle,SqlServer etc Oracle, SQL Server

Operating System: Linux

Other: CI/CD Pipeline (Git, Gradle, Docker,Jenkins,Kubernetes etc.)

CURRENT WORK EXPERIENCE:

Confidential

Data Engineering / Data Science Sr. Manager

Responsibilities:

Lead the data analytics team for infrastructure analytics program. Responsible for data delivery from data sourcing, data transformation to data modeling and validation.
I was responsible to Architect the AWS platform to ingest data into Confidential platform. My team developed a scalable framework to ingest daily data volumes of 30GB+ from globally distributed data sources into a consolidated model optimized for analysis and visualization.
Manage data science projects ranging from business reporting/business intelligence to statistical modeling/predictive science • Coach AWS Developer, junior data scientists and collaborate with regional functional leads in developing knowledge - sharing programs to facilitate a healthy organizational development

Confidential

Data Analytics Sr. Manager/Lead Architect

Responsibilities:

Review and drive the overall AWS big data architecture around data lake, perform analysis on that data for the use-cases provided by the client and create visualization/dashboards/reports
Drive data lake creation including tasks related data ingestion, data manipulation and data transformation
Review client requirements / use-cases and derive what can be achieved and demo'ed in 6 weeks
Do initial data analysis and work with client business teams to understand the attributes that need to be derived from the data
Be end to end responsible for execution of the PoC
Communicate with all stakeholders at least on weekly basis with status of the PoC
Project Management: agile methodology, resource planning and financial management
Data Engineering: AWS, Kinesis, AWS Data Lake, Athena, Lambda, ELK, AWS Glue,

Confidential

Data Analytics Sr. Manager

Responsibilities:

Provided proposal and road map to assist with a big data migration from a Teradata environment to a Hadoop Big Data platform (Corner Stone Project) for Confidential

Confidential

Lead Data Architect

Responsibilities:

Involved in building a Data analytics platform for analyzing WIFI signals strength on train sets for Confidential .
The Cloud Services to store and process data, Cloud Services to extract, transform and load data into EDW.
Relate each key measure with Location, Mile-marker and Station to provide the required data set.
Aggregate the data sets to provide intelligence by time periods, etc.
Generate the visualizations that use the data set to provide required information.
Also involved in planning and providing technical guidance for Confidential to migrate from on-prem to cloud their existing Datawarehouse project with Oracle/Informatica/Tableau tool.
Data Engineering: lambda, python/spark, AWS Data pipeline, AWS Glue, Informatica, Tableau.