Lawrence Karongo - Data Portfolio

Relevant Experience

Business Intelligence Engineer II – Fulfillment Technology AI, Amazon

04/2024 – 01/2026

Developed executive-facing QuickSight dashboards with self-service capabilities, enabling stakeholders to explore operational, business, and model performance data independently, reducing ad-hoc reporting requests by enabling data-driven decision-making.
Designed and put into production in Redshift data models specific to KPI monitoring and dashboarding given iterative stakeholder feedback.
Built a data model specific to a damage detection Computer Vision model deployed in Fulfillment Centers across NA (North America) and EU (Europe) worth an entitlement/cost savings over $10 million a year.
Implemented ROI and forecasting simulations to model entitlement gains and long-term business impact, guiding strategic prioritization and supply chain resource allocation across fulfillment centers and SHV1, Amazon's Flagship facility.
Produced the key analysis identifying the core metric needs for NA and the Flagship facility detrimental to automated Inventory Control Quality Assurance process, with an entitlement across NA over 1 billion dollars.
Defined and owned core QuickSight KPI frameworks for supply chain inventory for item image coverage, applying experimental analysis and causal reasoning to quantify incremental impact and justify leadership a $1 million dollar investment.
Designed and implemented AWS Glue and Spark Framework ETL pipelines, unifying multiple S3 and data lake sources to support supply chain metric production, experimentation analysis, and QuickSight dashboard automation.
Productionalized Spark SQL pipelines to deliver timely data model outputs aligned with program requirements, ensuring data reliability for downstream analytics and leadership QuickSight reporting.
Delivered multiple critical and high visibility QuickSight analytical products focusing on Computer Vision across three domains in six months, demonstrating strong prioritization, cross-functional communication, and end-to-end ownership.
Onboarded to the Amazon Cline Visual Studio extension for GenAI collaboration while building datasets using Airflow DAGs.

Business Analyst II – Selling Partner Services, Amazon

06/2022 – 04/2024

Collaborated closely with immediate and cross functional team to redesign antiquated data model for critical Seller Satisfaction metric, producing datasets with fact tables and dimension data.
Produced QuickSight dashboards and maintained feedback loop with stakeholders for metric updates and continued reporting needs.
Conducted customer (seller) journey and funnel analysis using behavioral data to identify friction points and conversion opportunities, informing product and engagement strategy.
Developed a large-scale NLP sentiment data model integrating open-text models to surface behavioral signals enabling targeted interventions based on member feedback patterns and reported in QuickSight.
Built automated metric deviation alerting tool improving operational response times and enabling proactive issue identification.
Conducted ANOVA and regression-based analyses to evaluate predictive power of trailing metrics on seller satisfaction, strengthening forecasting accuracy and engagement modeling.
Designed and maintained ETL pipelines supporting experimentation, feature engineering, and automated QuickSight reporting across multiple business lines.

Analytics Engineering – Independent Contractor, LK Data Solutions

01/2026 – Present

Started own LLC and retained first major client, supporting their data needs for Amazon Delivery Services across Spokane, Washington.
Architected and deployed end-to-end data model automating DOT compliance monitoring for trucking operations, processing weekly timecard data through S3, Lambda, Glue, Athena, QuickSight workflow with EventBridge Orchestration.
Engineered PySpark ETL pipelines implementing complex DOT regulatory business logic including rolling 7-day hour calculations, consecutive workday detection, and breaktime violation tracking across multiple aggregation levels (shift, daily, weekly).
Designed semantic layer in QuickSight with 15+ KPIs and level aware aggregations, enabling self-service exploration of overtime trends, violation patterns, and drive compliance status across configurable date ranges.
Implemented incremental data processing with merge/upsert logic using shift-level composite keys and timestamp-based deduplication, ensuring accurate historical tracking while supporting retroactive timecard corrections.
Built production-grade data quality controls including multi-format timestamp parsing (handling inconsistent CSV exports), PTO hour normalization, and role-based filtering to maintain data integrity across pipeline stages.
Delivered fully automated compliance monitoring system enabling real-time violation alerts and reducing manual timecard review overhead for fleet management.
Leveraged Claude Code (GenAI) to automate tasks.

Other Experience

Institutional Data Analyst, Charles R. Drew University of Medicine and Sciences

06/2020 – 06/2022

Extracted and processed data from university-wide systems and survey tools for exploratory data analysis.
Monitored, predicted and reported on student enrollment, retention, and graduation rates.
Ran classification models to identify missing data for reporting purposes.
Created data visualizations and presentations for institutional executive management.
Maintained data repository for year-round National Center for Education Statistics (NCES) reporting.
Power BI Dashboard implementation and ETL production and maintenance.
Supported creation of institution data warehouse.
Data engineering – transforming flat files into dynamic workflows to pipe into databases and feed reports.

Data Scientist, East Bay Community Law Center (EBCLC)

08/2018 – 06/2020

Sole analyst and modeling expert for the "Keep Oakland Housed" Collaborative on behalf of EBCLC.
Provided recommendations on organization-wide policy and process improvement through data evaluation.
Curation of data capturing methods for foundation and government grants reporting and research.
Implementation of regression analyses determining factors impacting housing and homelessness.
Statistical approaches applied to understand potential outcomes due to free/clinical legal representation.
Identified proper case management for org wide use and was technical lead on the data capture curation process (intake) as well as data migration.
Persuaded leadership to invest initial $70K on case management system with an additional yearly investment of $12K to drive proper data collection for robust analytical approaches.
Built Python applications to support storytelling for geographical housing impacts and to support further ad hoc data capturing.

Management Analyst, Minnesota Department of Transportation

06/2017 – 07/2018

Graduate Research Assistant, University of Minnesota Twin Cities

08/2014 – 05/2017

Education

Master of Public Policy

University of Minnesota Twin Cities

Bachelor of Science, Applied Economics

University of Minnesota Twin Cities

Tech Stack & Skills

QuickSight, Python, Power BI, SQL, Tableau, PySpark, AWS Glue, A/B Testing, Redshift, Funnel Analysis, S3, Seller & Customer Analytics, Athena, AWS Bedrock, SageMaker, Segmentation, Simulation, Cline AI Coding, Claude Code