Looking for a job

Ajala Boluwatife

@ajalaboluwatife

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I build automated data pipelines, predictive ML models, and faster CI/CD workflows that deliver measurable outcomes.

Nigeria

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What I'm looking for

I'm seeking a data analyst, machine learning engineer, or data engineering role where I can use statistical modelling, Python, SQL, and build automation to drive measurable business outcomes.

I'm currently improving build and release delivery at Sephora Systems, where I reduced pipeline execution time by 35%, improved reliability to 99.2% uptime, and accelerated feature delivery by 40%.

I also created reusable configuration templates and documented build procedures in a centralised Git repository, reducing new-engineer environment setup time by 60%.

During my data analytics placement at the Nigerian Communications Commission, I replaced manual spreadsheet ETL with Python pipelines, cutting reporting turnaround time by 50%. My validation and cleaning workflow improved regulatory dataset accuracy by 30%, while SQL optimisation reduced ad-hoc query resolution time by 45%.

I've built 10+ SQL and machine learning projects, from analysing 6.4 million NYC taxi trips to developing a diabetes prediction pipeline with 92% accuracy. I use Python, SQL, PostgreSQL, Pandas, Tableau, and machine learning tools to turn complex data into practical insights and working systems.

Experience

Work history, roles, and key accomplishments

Current

Build Engineer

Current

Sephora Systems

May 2026 - Present (4 months)

Reduced build pipeline execution time by 35% and improved system reliability to 99.2% uptime by designing automated CI/CD workflows and standardizing environment configurations. Accelerated feature delivery by 40% and reduced new engineer setup time by 60% through documentation and reusable templates.

CI CD Build Automation Git Version Control Root Cause Analysis Configuration Management Scripting Dependency Management

Data Analytics Intern (SIWES)

Nigerian Communications Commission

Jan 2023 - Dec 2024 (1 year 11 months)

Improved reporting turnaround time by 50% by building automated data pipelines in Python, and increased data accuracy by 30% through statistical validation and data-cleaning workflows. Delivered insights adopted into 3 quarterly reports and reduced ad-hoc query resolution time by 45% via SQL optimization.