Mike D. Email and Phone Number
Technology Stack :✓Front End: JavaScript, HTML, CSS, Python and Shell , XML.✓Back End: Java, Python, Scala, HTML.✓Database: MySQL, MS SQL, NoSQL , Oracle , Microsoft SQL Server ✓Cloud Platform: AWS (ECS, Lambda, EC2, ECR, ECS, S3, SQS, SNS, SES, API Gateway) Glue , Snowflake, Cloud Watch, Microsoft Azure.✓CI/CD: Jenkins, Docker Containers, Apache Airflow.✓Big Data Ecosystems : Hadoop , HDFS , HBase, Hive , Pig , Sqoop, Map Reduce , Kafka , Cassandra.Technical Expertise13+ years of experience in IT Industry with expertise in Data warehousing, Relational database, ETL development & administration, Business Intelligence Technologies, Architecting and developing cloud-based solutions (AWS & Azure) and DevOps.• Solid understanding of database design principles, experienced in all phases of Data warehouse development, planning, gathering requirements, design and implementation.• Strong knowledge of Informatica ETL design, development, administration and performance optimization.• Experienced and hands-on experience with architecting and developing cloud-based solutions, sysops, CloudWatch monitoring, programming using AWS CLI and python SDK’s, Creating and maintaining DataLake in S3, CloudFormation, Serverless computing using AWS Lambda functions and API Gateway, Dynamo db, Ansible playbooks for automated deployment, Jenkins for Continous Integration.• Experience in python programming and bash scripting.• Ansible for configuration management and deployment of scripts across multiple environments.• Understanding of AWS Glue and AWS DMS services.• Understanding of containerization concepts – Kubernetes and Docker.• Certified AWS Solutions Architect Associate, AWS Sysops Administrator Associate.• Extensive experience working with AWS Redshift Data Migration, Performance tuning in Redshift, Tuning MicroStrategy report queries following Redshift best practices to receive optimal performance.• Understanding of AWS Spectrum to efficiently query and retrive data from files in S3.• Understanding of AWS Kinesis for streaming and processing real time logs.• Strong ability to understand and convert the logical data modeling into ETL data flow and draw a proper mapping to physical data model.• Ability to design the ETL considering factors like high data volume, partitioned data, join effectiveness, parallelism.• Currently working at AWS with Data engineering experience in building highly scalable distributed systems of data extraction, ingestion, and processing of large data sets.
Kaiser Permanente
View- Website:
- kaiserpermanente.org
- Employees:
- 120876
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Lead Aws Data EngineerKaiser Permanente Jul 2022 - PresentLos Angeles, California, United StatesLead multiple data engineering teams in designing data platform .• Collaborated with the Product Owners in understanding the requirement and design a data flow system. • Built out a Data Lake on Databricks using Databricks Delta tables stored in AWS S3. • For real-time processing used Kafka and Spark Structured Streaming. • Used AWS Lambda for event driven processing. • For batch-processing used Spark/Scala based jobs with Airflow Orchestration • Used DataFrame, Dataset & Spark SQL APIs with Spark & Scala for data transformation. • Optimized Spark based jobs by partitioning, bucketing, z-ordering the delta tables. • Optimized AWS spend by using EC2 spot instances for processing and S3 life cycle for data storage. • Spark jobs merged data to Snowflake and Elasticsearch. • Used Elasticsearch for search capability of the marketplace portal. • Used Snowflake Marketplace to share datasets with external clients. • Used Datadog and Cloud Watch for monitoring and logging. • Used SAML, AWS IAM role and Databricks SSO feature to manage governance and access control to Data Lake. • Used GIT for version control • Used Azure DevOps for automation/CICD pipeline
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Sr Aws Data EngineerMastercard Dec 2019 - Jun 2022New York, United States• Design, architect and implement Data Warehousing and reporting solutions• Design, automate, build, and launch scalable, efficient and reliable data pipelines into production • Have built data extraction process using python libraries such as imbox, pymongo,simple-salesforce• Have built explores, data visualizations using Looker• Have 3 plus years of experience in AWS technologies• Build batch, near real-time data and reporting solutions• Lead and participate in design discussions and meetings• Mentor data engineers and analysts• Have performed data analysis using python libraries such as simple-salesforce• Design, build and enhance dimensional models for Data Warehouse and BI solutions• Research new tools and technologies to improve existing processes• Develop new systems and tools to enable the teams to consume and understand data more intuitively• Partner with engineers, project managers, and analysts to deliver insights to the business• Perform root cause analysis and resolve production and data issues• Create test plans, test scripts and perform data validation• Tune SQL queries and ETL pipelines• Build and maintain data dictionary and process documentation -
Aws Data EngineerThe Home Depot Mar 2016 - Nov 2019Atlanta, Georgia, United States -
Data EngineerBroadcom May 2012 - Feb 2016San Jose, California, United States -
Data AnalystFord Motor Company Jul 2010 - May 2012Dearborn, Michigan, United States
Mike D. Education Details
Frequently Asked Questions about Mike D.
What company does Mike D. work for?
Mike D. works for Kaiser Permanente
What is Mike D.'s role at the current company?
Mike D.'s current role is Lead Data Engineer | Python | PySpark | PostgreSQL | AWS | Data warehouse | Bigdata | Databricks | Snowflake |.
What schools did Mike D. attend?
Mike D. attended Stanford University.
Who are Mike D.'s colleagues?
Mike D.'s colleagues are Phoenicia Bullock, Travisa Stewart, Bryce Kauffman, Brian Wright, Venkat Koneru, Mark Sponsler, Stephen Bettini.
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Michael D.
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