Donald Oriti Jr

Responsibilities:

• Lead team of 30+ data and platform engineers as well as multiple Sr. Managers and Directors
• Set the vision for the data platform from cloud infrastructure to IaC templates and data product design
• Partner with and enforce data governance across all aspects of the platform
• Partner with CISO to ensure security within the cloud environments and set data access policies
• Construct cross-functional scrum teams to support new and existing business capabilities from application support to data publishing and curation
• Develop product roadmaps, manage hiring quotas and workforce planning, and set/manage cloud infrastructure budget

Projects/Achievements:

• Built the Data Platform team from a group of eight engineers to a team of over 30 and growing
• Designed data platform infrastructure using a distributed AWS account model as well as the Data Mesh and Lakehouse design principals
• We partnered with our IT infrastructure and security teams to build out AWS account templates for consistent and secure account creation and deployment
• We created the CICD infrastructure and automated testing framework used across the IT department
• We developed CDK templates and CICD patterns for deploying our data publisher stack in AWS supporting both batch and streaming data
• Implemented a governed central Glue data catalog using Lake Formation
• Implemented Alation as our data dictionary, documentation store, and data quality monitoring. In Alation, we also track data lineage from source system to Tableau dashboard
• Implemented Data Manager for Tableau to present status on data sources from Alation around most recent data, data quality, etc.
• Implemented Databricks as both a compute platform for data engineers as well as a DS/ML toolbox for practitioners enterprise-wide
• Set data governance policies for data naming conventions, SLAs/SLOs, and data publishing
• Built out Data Engineering toolsets leveraging MWAA, Databricks, dbt, etc.
• Began producing highly-governed data products that abstract away the complexities of source data and provide a consistent, easy-to-use experience for analysts, report developers, and other systems

Responsibilities:

• Lead team of 14 engineers "embedded" with Analytics/ML teams providing engineering support
• Perform third-party data ingest like advertising partners and data aggregators
• Develop analytics and enterprise data products in Hadoop/S3
• Develop/maintain Redshift DWs for analytics and interactive Tableau dashboards
• Advise on and lead data governance efforts for business logic/calculations, data production, and data consumption
• Direct data production/consumption best practices for both upstream sources and "last-mile" analytics
• Lead training/discussion sessions to better help Analysts and Data Scientists on data self-service best practices and query optimization
• Act as business/requirements analysts on large projects to design and develop data sets in the data lake through raw/clean/enterprise/analytic layers, partnering with data producers and consumers
• Provide strategic vision on the evolution of our data architecture including tooling for Business Analytics, ML Feature Repositories, Data Quality/SLA tracking, etc.

Projects/Achievements:

• Created "prototype-to-production" pipeline to reduce development times from months to weeks or days
• Migrated analytics environment from on-prem Hadoop to AWS (S3/Redshift) using Airflow (MWAA) and EMR
• Implemented enablement framework to provide a path to production for analyst-built data sets
• Implemented "roundtables" and Data Engineering office hours sessions to facilitate discussion and knowledge sharing
• Evangelized "data mesh" architecture, implementing the design with telemetry data
• Created analytics "semantic layer" to better organize approved data sets and increase discoverability by analysts
• Developed/implemented Redshift access governance to promote analyst collaboration while maintaining proper access controls and a clear delineation of approved/production and user-produced data
• POCed and implemented Redshift Data Sharing to reduce ETL load on the analytics cluster improving query performance and Tableau load times
• Implemented "containerized" business logic objects centralizing calculations in normalized data sets to facilitate Data Science/ML Feature creation and provide reusable components for data consumption and improvements in data quality
• Implemented data infrastructure for Marketing Operations using our team's Data Science feature/model framework for audience selection in Salesforce maintaining alignment with Analytics/Data Science
• Grew the size of team from just two engineers in 2018 to 14 in 2021 (including 4 promotions) with engineers working remotely nationwide

Responsibilities:

• Support Customer Marketing through dashboards, scorecards, and ad hoc analyses for experimentation, customer insights, and self-serve reporting
• Support Renewals reporting and analytics discovering opportunities in terms of leakage and business processes affecting customer retention
• Drive initiatives with the Enterprise Data team including analytic data architecture, workflow processes to speed development, and future initiatives to streamline/advance marketing and analytics
• Develop insights creating better marketing audiences for targeted communications

Projects/Achievements:

• Implemented experimentation measurement process using Bayesian statistics allowing marketers to "micro-target" segments
• Rebuilt aging e-mail reporting marts to better reflect our platform landscape and improve data quality using PySpark and Oozie
• Developed a renewals testing platform streamlining experimentation evaluation reducing turnaround times from days to hours

Responsibilities:

• Provide administrative support for the marketing platform managing application servers and metadata management
• Provide data engineering support by extracting data from various sources (Hadoop, SQL Server, etc.) and landing data in Teradata to feed the marketing platform
• Landing marketing platform contact history from Teradata in the Hadoop cluster for analytics using TDCH and Oozie
• Support Finance data engineering needs in Teradata

Projects/Achievements:

• Successfully implemented the RedPoint Interaction platform, replacing Teradata Customer Interaction Manager.
• Collaborated with Front-of-Site and the Outbound Communications team on an alternative data process for abandoned cart data enabling more timely customer communications while reducing load on eComm APIs.
• Developed upstream data processes with the Outbound Communications Team to handle e-mail scrubbing for non-platform execution reducing negative reputation events and potential legal exposure.
• Collaborated with Front-of-Site and other business partners on enhancing/streamlining our prospect process improving our reach to non-GoDaddy shoppers.
• Streamlined NPS data ingress/egress from Satmetrix enabling self-serve survey.

Responsibilities:

• Architect data mappings and build, organize, and approve report objects in MicroStrategy (MSI) adhering to our data governance standards
• Develop training materials and organize training sessions for MicroStrategy and reporting company-wide.
• Work with various business partners on reporting requirements and translating those to data needs for engineering.
• Develop standard, base reporting in various subject areas for use in operations.

Projects/Achievements:

• Rolled out several MSI upgrades moving primary use through the web saving costly software rollouts
• Created training sessions and one-on-one sessions to onboard new employees as well as advanced reporting classes
• Implemented reporting for Transportation from data ingress to exposure in MSI giving business partners visibility to transportation cost impacts at a line-item level for the first time.
• Collaborated with data engineering on a streamlined intake and prioritization process which improved communication to the org on projects timelines and process.

Responsibilities:

• Execute marketing campaigns via the marketing platform (Teradata CIM) against various channels (POS, SMS, e-mail, digital coupons).
• Develop and manage the analytic semantic layer in the Teradata CDW.
• Manage the marketing platform metadata structure.

Projects/Achievements:

• Implemented the Teradata CIM platform.
• Designed and developed a Marketing Relationship Management application using C# and Teradata allowing us to organize campaign information and integrate data processes with CIM and streamline analytics.
• With analytics, developed models to implement the Offer Optimizer functionality in CIM to pick the best offers for shopper in a marketing campaign.
• Developed QA processes with analytics to ensure targeting analyses were accurate improving the quality of campaign results.

Responsibilities:

• Develop and evaluate marketing campaigns.
• Perform deep-dive analytics on customer activity pre/post store events (assortment changes, competitor openings, etc.).
• Develop testing and measurement best practices and analytics methodology.

Projects/Achievements:

• Worked with data engineering and Customer Insights to design and implement the data model used in the Teradata CDW.
• Developed our campaign ROI methodology and calculations.
• Developed automated data processes using BTEQ scripts and SSIS to produce campaign results for speed of analysis.