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Hussein Akanji

@husseinakanji

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Senior Data Engineer specializing in reliable ETL/ELT pipelines and analytics-ready warehouses.

United States

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What I'm looking for

I’m looking to lead production-grade data pipeline and warehouse work—strengthening data quality, governance, and operational resilience—so analytics teams can trust curated datasets and dashboards with predictable refreshes.

I’m a Senior Data Engineer with 10+ years of experience building and maintaining production-grade ETL/ELT pipelines, dimensional warehouse models, and analytics-ready datasets. I focus on pipeline reliability, data quality, governance, and operational resilience across SaaS and enterprise analytics environments.

At Peloton Interactive, I designed and stabilized warehouse refresh pipelines, redesigning ingestion and transformation layers into raw, refined, and curated stages. This reduced late dashboard refreshes by 28%, while I also implemented CDC and incremental loading strategies to minimize full-refresh dependency. I integrated external APIs and semi-structured JSON feeds with schema validation and late-arriving logic, reducing ingestion failures by 31%.

I’ve built and optimized analytical warehouse models—fact tables, dimension tables, and reusable curated marts—to support customer lifecycle, subscription reporting, and engagement analytics. By tuning SQL and Spark transformations (joins, clustering, and partition handling), I improved reporting query performance by 35%. I also developed reusable data quality validation frameworks (freshness checks, duplicates, referential integrity, schema consistency) that reduced recurring analytics data incidents by 24%.

Earlier roles strengthened my end-to-end craft: designing incremental ingestion frameworks, micro-batch workflows, and monitoring that reduced investigation effort by 18%. I bring a collaborative mindset with product managers, analysts, and platform teams—translating reporting requirements into maintainable implementations, documenting lineage and ownership for RBAC, and improving release reliability through operational standards and deployment checklists.

Experience

Work history, roles, and key accomplishments

Senior Data Engineer

Peloton Interactive

Mar 2023 - Apr 2026 (3 years 1 month)

Designed and maintained production ETL/ELT pipelines across 30+ data sources, stabilizing warehouse refresh after SLA failures and reducing late dashboard refreshes by 28%. Implemented CDC and incremental loading, integrated JSON APIs with schema validation (reducing ingestion failures by 31%), and improved query performance by 35% while reducing recurring analytics incidents by 24%.

Python SQL Apache Spark Apache Airflow CDC API Integration

Data Engineer

Peloton Interactive

Feb 2020 - Mar 2023 (3 years 1 month)

Built scalable ETL/ELT pipelines with Python, SQL, Spark, and Airflow to ingest operational, customer, marketing, payment, and engagement data into centralized analytics environments. Developed CDC-based incremental ingestion frameworks (cutting nightly processing windows by 22%) and improved refresh performance by 25%, while reducing recurring investigation effort by 18%.

Python SQL Apache Spark Apache Airflow CDC Patterns Watermarking Incremental Ingestion Data Modeling

Data Science

World Resources Institute

Aug 2018 - Feb 2019 (6 months)

Prepared analytical datasets for climate, transportation, and energy research by ingesting public APIs, relational datasets, CSVs, and geospatial sources using Python and SQL workflows. Built reusable transformation and validation routines that standardized inconsistent inputs and reduced manual effort by ~30% while improving dataset trustworthiness.

Python SQL Data Ingestion Data Validation data transformation Geospatial Data Feature Engineering

Bibliometrics Data Analysis

Wiley

Feb 2018 - May 2018 (3 months)

Built reproducible text and metadata processing pipelines for publication analysis, cleaning article metadata, authors, abstracts, and citation attributes using Python and SQL. Reduced manual metadata cleanup through staged transformations, deduplication rules, and validation checks, producing model-ready outputs for topic and publication trend discovery.

Python SQL Metadata And Deduplication Validation Text Cleaning Keyword Extraction Topic Preparation

Data Engineering & ML

MJ Intelligence System Ltd

Jun 2016 - Aug 2016 (2 months)

Supported data migration and preprocessing workflows by profiling source data, mapping fields, cleaning invalid values, and preparing validated outputs for downstream reporting and ML experiments using SQL and Python. Implemented validation routines (row counts, duplicate keys, missing fields, referential checks) to reduce rework during handoff and documented source-to-target mappings for clearer

SQL Python Data Profiler Data Migration Data Cleaning Source To Target Mapping Data Validation

Data Analyst

Michelin

Jul 2013 - Jan 2015 (1 year 6 months)

Analyzed manufacturing and process data with SQL and Excel reporting workflows to track production quality, throughput, defect trends, and operational KPIs across plant teams. Built recurring KPI dashboards and automated weekly reporting via SQL extracts, reducing manual report assembly time by ~6 hours per week.

SQL Excel KPI Reporting Defect Trend Analysis Root Cause Analysis Report Automation Data Quality Process Monitoring

Education

Degrees, certifications, and relevant coursework

Stevens Institute of Technology

Master's Degree of Business Intelligence & Analysis, Business Intelligence & Analysis

2016 - 2018

Master's degree in Business Intelligence & Analysis at Stevens Institute of Technology. Completed coursework focused on business intelligence and analytics.