NY

Tech: R, SQL, dbt, Apache Airflow, Python, Postgres, MongoDB, AWS
- Planned and implemented extensive data pipelines in R within an ETL framework that monitored outcomes for more than 800,000 New York City students
o Monitored and managed shelter usage date pipeline, permitting a measurement of user engagement with our data platform and identifying opportunities for increased user training and awareness
o Coordinated Transformation and Loading of academic and demographic data to augment data comprehensiveness, diminish response time, and add insights for stakeholders
o Assembled and combined large data sets to produce output tables used to answer Project Manager enquiries and meet stakeholder expectations
o Identified, designed, and implemented migration scripts and updated data dictionaries to accommodate changes in data table shapes resulting in increased comprehensiveness of data collected
- Implemented consistency checks and accompanying notifications to ensure the reliability, freshness, and coherence of data pipelines
o Created informative webhooks in Slack and monitored pipelines in Airflow to enable quick troubleshooting of pipelines and the resolution of data issues or inconsistencies
o Deployed extensive confirmation checks of data coherence and conformity to business logic within each stage in the data transformation process
- Created and maintained DAGs in Apache Airflow executing tasks and their dependencies to ensure deadline-driven completion of data processing
- Engaged in cross collaborative work with different functional teams in the IT department
o Collaborated with engineering and analytics teams to create and refine schemas for loading data into NoSQL environment in MongoDB
o Produced reports for Project Managers about the content of our data assets and flagged any issues that would need to be addressed with vendors before the ingestion and processing of data
o Built, maintained, and deployed data products for analytics team and then further downstream into software engineering for loading into proprietary data platform
o Documented ETL processing along with data flows and visualizations for future reference and refinement in Confluence and StackOverflow
- Utilized data build tool (dbt) models to monitor and troubleshoot summer attendance data pipeline to prevent data delays, meet stakeholder expectations, and maintain the day-to-day freshness of data

Planned and implemented extensive data pipelines in R within an ETL framework that monitored outcomes for more than 800,000 New York City students
Monitored and managed shelter usage date pipeline, permitting a measurement of user engagement with the data platform and identifying opportunities for increased user training and awareness
Coordinated Transformation and Loading of academic and demographic data to augment data comprehensiveness, diminish response time, and add insights for stakeholders
Assembled and combined large data sets to produce output tables used to answer Project Manager enquiries and meet stakeholder expectations
Identified, designed, and implemented migration scripts and updated data dictionaries to accommodate changes in data table shapes resulting in increased comprehensiveness of data collected
Implemented consistency checks and accompanying notifications to ensure the reliability, freshness, and coherence of data pipelines
Created informative webhooks in Slack and monitored pipelines in Airflow to enable quick troubleshooting of pipelines and the resolution of data issues or inconsistencies
Deployed extensive confirmation checks of data coherence and conformity to business logic within each stage in the data transformation process
Created and maintained DAGs in Apache Airflow executing tasks and their dependencies to ensure deadline-driven completion of data processing
Engaged in cross collaborative work with different functional teams in the IT department
Collaborated with engineering and analytics teams to create and refine schemas for loading data into MongoDB
Produced reports for Project Managers about the content of data assets and flagged any issues that would need to be addressed with vendors before the ingestion and processing of data
Built, maintained, and deployed data products for analytics team and then further downstream into software engineering for loading into proprietary data platform
Documented ETL processing along with data flows and visualizations for future reference and refinement in Confluence and StackOverflow
Utilized dbt models to monitor and troubleshoot summer attendance data pipeline to prevent data delays, meet stakeholder expectations, and maintain the day-to-day freshness of data

Managed a portfolio of 13 lowest performing public schools in New York City’s Renewal and Community Schools Initiative by facilitating the use of Google Apps based data tools
Ensured accuracy and integrity of data for enabling school leadership to make informed decisions on meeting crucial district wide benchmarks
Managed organization-wide dashboards and spreadsheets and monitored data dependencies to ensure accuracy and freshness of data in pipelines
Produced visualizations of academic and attendance trends within Google Sheets and Tableau to provide insights on real-time organizational progress in meeting New York City’s Renewal and Community Schools Initiative expectations
Built relationships with organizations and staff over several years to better understand their technological and data needs to enhance the durability and impact of provided support
Conducted school leadership team meetings to monitor academic and attendance outcomes and implement data-guided decisions to maintain, improve, or bolster progress
Conducted 100+ Strategic Data Check-ins with organizations preemptively flagging issues concerning academic achievement, attendance outcomes, and course scheduling to help organizations reach their Renewal and Community Schools Initiative goals
Facilitated the training of users in Google Sheets features such as sorting, filtering, pivot tables, and chart construction while also offering instruction on statistical concepts to help school leaders interpret available data and determine next action-steps
Designed ad-hoc requested dashboards and other data visualizations in Google Sheets that conveyed information on schools and student progress to help drive data-driven decisions and identify time-sensitive priorities

Student performance in Chemistry and Physics showed a >40% percent improvement over the same time during the previous year; student performance showed >30% improvement in Biology in comparison to the preceding year
Employed STATA and EXCEL presentations, charts, and spreadsheets to introduce approaches to using data to test hypotheses and extract insights about effective practices
Digitized curriculum and established an easy-to-access website via Google Drive and ancillary frameworks to coordinate classroom activities and establish appropriate academic expectation
Deployed Excel Data Tracker to identify students’ strengths and weaknesses along granular levels of content and used these insights to differentiate student instruction
Teach for American Cohort Member 2012
Selected as one of 5,000 accepted candidates out of 47,000 applicants to teach in an urban or rural school district nationwide
Attended more than 40 hours of rigorous professional development to cultivate skills in implementing classroom management, interpreting crucial student achievement data, and curriculum planning and design

Prepared proposals synthesizing primary and secondary research to deliver actionable intelligence on health care topics
Edited and organized company reports, conference presentations, RFIs, and RFPs to ensure clarity, concision, and factual accuracy
Extracted and analyzed primary and secondary information from databases such as Lexis-Nexis, Factiva, and Thomson Reuters
Gathered and evaluated qualitative and quantitative data and edited their presentations in PowerPoints and White Paper deliverables

Classified variables for a 300,000 item data set to prepare it for quantitative analysis
Collected, organized, and input categorical and quantitative variables into massive database
Obtained, analyzed, and graphed data according to publishing requirements in preparation for submission to a peer-reviewed journal
Conducted multivariate regressions to assess fledgling hypotheses using STATA, R, and SPSS
Prepared literature reviews summarizing key concepts and constructed graphical displays to comprehensively present information