OLU BABACAMP
Washington
Summary
Data analyst with over 5 years helping clients turn massive event streams into real business wins. I love digging into user behavior, running clean A/B tests, and building tools that let clients make smarter decisions fast. Strong in SQL, Python analysis, and big data environments (Databricks, Athena, ClickHouse). I’ve consistently delivered measurable lifts in conversion and retention while working closely with engineering, customer success, and product teams to get integrations right the first time. Adept in providing analytical support to ensure all decisions align with business metrics and drive product innovation.
Overview
9
9
years of professional experience
Work History
Senior Data Analyst
Partigard
06.2022 - Current
- Ran multiple high-stakes A/B tests on search and recommendation features, consistently hitting 12-22% lifts in key conversion metrics by carefully designing variants and controlling for seasonality/user segments. This work directly influenced key performance indicators for client success.
- Took ownership of data quality during customer onboarding — spotted tracking gaps early, worked directly with client engineers to fix them, and built validation scripts that cut ramp-up issues , enhancing supportability and reliability of the data pipeline.
- Created real-time dashboards in Cube.js + ClickHouse that provide clear views into user journeys and performance metrics; these supported faster iteration on product features across accounts..
- Explored TB-scale event data to uncover unexpected user patterns, then proposed and prototyped small UX changes that engineering later rolled out, dramatically boosting engagement noticeably. This was a true initiative in data exploration and exploratory data analysis.
- Partnered daily with Customer Success to answer questions quickly, turning raw feedback into prioritized insights that made tests more successful and clients happier, showcasing strong organizational and communication skills.
- Presented actionable insights to stakeholders, driving strategic planning and operational improvements.
- Partnered with IT teams to ensure seamless integration between databases and analytical tools, maximizing system efficiency across departments.
- Fully Remote
Data Analyst
Enterpriselinked
New York, NY
01.2020 - 05.2022
- Built and maintained end-to-end data pipelines in Spark/Hive to feed client-facing analytics; these dashboards became go-to tools for understanding unique shopping behaviors that standard platforms missed, enhancing overall reporting quality and efficiency.
- Collaborated with integrations engineers to strengthen event tracking for search models; cleaned up the data foundation and reduced downstream errors, reflecting my initiative in tackling this challenge.
- Collaborated with cross-functional teams to enhance data collection processes and reporting accuracy.
- Turned customer interview notes and usage data into quick prototypes for dashboard improvements, which led to faster, more confident decisions from merch teams.
Big Data Engineer
Tech Startup
San Francisco, CA
07.2017 - 12.2019
- Designed and implemented initial ETL pipelines using Python and Spark to process and ingest streaming event data into AWS S3 and early ClickHouse setups — handled growing volumes up to several hundred GB/day.
- Built real-time log ingestion flows with AWS Kinesis and Lambda, ensuring reliable delivery and basic compliance checks for observability.
- Supported data infrastructure scaling by setting up simple orchestration with Docker containers and Git-based workflows; helped migrate batch jobs to more efficient formats, cutting processing times noticeably.
- Collaborated on foundational data lake components, including partitioning strategies and quality monitoring scripts that prevented downstream issues for the analysis team.
- Improved collaboration between teams by creating comprehensive documentation detailing technical aspects of various big data solutions.
- Optimized data processing by implementing Hadoop and Spark frameworks for big data management.
- Modeled predictions with feature selection algorithms.
- Applied loss functions and variance explanation techniques to compare performance metrics.
Education
PG - Data Science and Machine Learning
Massachusetts Institute of Technology (MIT)
Boston, Massachusetts01.2022
BSC - Business Management
University of Maryland
Adelphi, Maryland, USA01.2018
Skills
- SQL (Presto/Athena, ClickHouse, Hive)
- Python (pandas, NumPy, seaborn/matplotlib) & PySpark
- A/B Testing & Statistical Analysis
- Dashboarding & BI (Cubejs, Tableau, Google Analytics)
- Big Data Tools (Databricks, Spark, AWS S3/Lambda/Kinesis)
- Data Quality & Onboarding Verification
- Cross-Functional Communication (technical ↔ business)
- Git, Docker basics
- ETL processes
- Data warehousing
Timeline
Senior Data Analyst
Partigard
06.2022 - Current
Data Analyst
Enterpriselinked
01.2020 - 05.2022
Big Data Engineer
Tech Startup
07.2017 - 12.2019
BSC - Business Management
University of Maryland
PG - Data Science and Machine Learning
Massachusetts Institute of Technology (MIT)