Built the end-to-end data pipeline for the MIRNet temperature forecasting research project: sensor readings were streamed from embedded hardware to a Raspberry Pi 4 via MQTT, preprocessed and cleaned in Python, and used to train a bidirectional LSTM deep learning model; the resulting IfeData dataset (Ile-Ife, Nigeria) was one of two datasets on which MIRNet achieved state-of-the-art forecasting accuracy, published in FUOYE Journal of Engineering and Technology, Vol. 9, No. 1 (2024)
Data Engineer
Full-time available
Bio
Adedayo A. is a Data Engineer with over three years of experience in building production data pipelines, backend APIs, and event-driven systems, particularly within fintech and SaaS environments. Proficient in Python, SQL, and JavaScript, Adedayo builds robust data warehousing and modeling solutions using BigQuery, Snowflake, Redshift, and PostgreSQL. He utilizes frameworks such as Flask, FastAPI, and Django for backend services and APIs, and manages orchestration with Prefect, Airflow, and Celery. His work includes…
Expertise
PythonSQLJavascriptFlaskFast APIGraphQLKubernetesAWSGCPSnowflakeBigQueryData Warehouse DesignETL PipelinesApache AirflowSeleniumNumPyWebScraping
Video Intro
Assessments
Projects
Built the data pipeline for Chemotronix, an IoT-enabled carbon emissions monitoring system: CO2 sensor readings were streamed in real time to ThingSpeak via API, preprocessed in Python (feature engineering on date columns, mean-encoding sectors, one-hot-encoding countries), and used to train a CatBoost gradient boosting model deployed on Heroku for emissions prediction; sourced and cleaned the initial training dataset from carbonmonitor.org to bootstrap the model before live IoT data was available
Built a dbt analytics pipeline on DuckDB to model Voice AI interaction data from Irembo, Rwanda's e-government platform: designed and implemented a fact_voice_ai_sessions dimensional model joining session data, user demographics, ASR confidence metrics, and application outcomes; the final fact table surfaces key accessibility and performance signals including misunderstanding rate, escalation flags, disability status, and first-time digital user indicators
Experience
Data Engineer
Stears· Enterprise & SaaSMar 2024 — Present- Own a production dbt project with 200+ SQL models structured across staging, intermediate, fact, and dimension layers, serving client-facing APIs for company, transaction, investor-profile, and country-risk products. Enforce data quality through Airbyte freshness validation macros, database constraints, and 35 dbt exposures documenting full data lineage across 7 product surfaces.
- Designed dimensional models for high-volume financial and time-series data, including a country-risk warehouse covering daily, weekly, monthly, quarterly, and annual macroeconomic indicators across 15+ African countries. Used star-schema patterns with surrogate keys and precomputed aggregates (revenue bands, EBITDA margins, transaction history), all materialized as indexed tables to eliminate runtime join latency and support millions of API reads with precision.
- Build and maintain 44 orchestrated Prefect 3.x pipelines processing end-to-end ELT workflows. Airbyte syncs ingest 61+ raw tables from operational sources into PostgreSQL, dbt transforms them into production-grade models, and downstream services (Qdrant semantic search, Algolia indexing, S3 documentation exports) consume the output. Implemented modular, reusable ELT patterns standardized across 20 domain modules with staged/production environment separation, enabling rapid onboarding of new data products without deep technical ramp-up.
- Built a reusable AI-driven data extraction subsystem using CrewAI and Claude that parses unstructured sources (PDFs, HTML, APIs) into Pydantic v2-validated datapoints across 19 specialized flows (CPI, inflation, FX, public debt, monetary policy). This enables Finance teams to receive structured, compliant financial data without manual processing.
- Led an end-to-end migration of the operational data layer from Airtable to NocoDB, rebuilding 199+ SQL models and re-architecting all Airbyte ingestion connections. This eliminated thousands in recurring tool costs while maintaining zero downtime for downstream financial data products
PythonELT/ETLPostgreSQLJavascriptdbtdata modellingCI/CDAWSDockerPrefectDBMLResearch Associate (Data Engineering)
Stears· Enterprise & SaaSAug 2022 — Dec 2023- Engineered an IoT data pipeline using MQTT and Python (Pandas, Polars, NumPy) to ingest real-time streaming sensor data and large-scale historical weather datasets, preparing inputs for MIRNet — a deep-learning temperature-forecasting model later published in the FUOYE Journal of Engineering and Technology.
- Developed preprocessing and feature engineering with Scikit-learn, optimizing data flow to an Nvidia GPU cluster for TensorFlow/Keras model training and supporting end-to-end data science workflows from ingestion through deployment.
PythonResearch WorkPandasPolarsMQTTData Streaming
Education
B.Sc. Electronics and Electrical Engineering
Obafemi Awolowo UniversityIle-Ife, Nigeria2017 — 2023
Data Engineer
Full-time available
Adedayo is available for hire
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Bio
Adedayo A. is a Data Engineer with over three years of experience in building production data pipelines, backend APIs, and event-driven systems, particularly within fintech and SaaS environments. Proficient in Python, SQL, and JavaScript, Adedayo builds robust data warehousing and modeling solutions using BigQuery, Snowflake, Redshift, and PostgreSQL. He utilizes frameworks such as Flask, FastAPI, and Django for backend services and APIs, and manages orchestration with Prefect, Airflow, and Celery. His work includes…
- Projects completed3
- Hourly rate
- 3-4 years experience
- Lagos, NGA
- Member since Jun 2026
Industries
Enterprise & SaaS
Expertise
PythonSQLJavascriptFlaskFast APIGraphQLKubernetesAWSGCPSnowflakeBigQueryData Warehouse DesignETL PipelinesApache AirflowSeleniumNumPyWebScraping
Video Intro
Assessments
Projects
Built the end-to-end data pipeline for the MIRNet temperature forecasting research project: sensor readings were streamed from embedded hardware to a Raspberry Pi 4 via MQTT, preprocessed and cleaned in Python, and used to train a bidirectional LSTM deep learning model; the resulting IfeData dataset (Ile-Ife, Nigeria) was one of two datasets on which MIRNet achieved state-of-the-art forecasting accuracy, published in FUOYE Journal of Engineering and Technology, Vol. 9, No. 1 (2024)
Built the data pipeline for Chemotronix, an IoT-enabled carbon emissions monitoring system: CO2 sensor readings were streamed in real time to ThingSpeak via API, preprocessed in Python (feature engineering on date columns, mean-encoding sectors, one-hot-encoding countries), and used to train a CatBoost gradient boosting model deployed on Heroku for emissions prediction; sourced and cleaned the initial training dataset from carbonmonitor.org to bootstrap the model before live IoT data was available
Built a dbt analytics pipeline on DuckDB to model Voice AI interaction data from Irembo, Rwanda's e-government platform: designed and implemented a fact_voice_ai_sessions dimensional model joining session data, user demographics, ASR confidence metrics, and application outcomes; the final fact table surfaces key accessibility and performance signals including misunderstanding rate, escalation flags, disability status, and first-time digital user indicators
Experience
Data Engineer
Stears· Enterprise & SaaSMar 2024 — Present- Own a production dbt project with 200+ SQL models structured across staging, intermediate, fact, and dimension layers, serving client-facing APIs for company, transaction, investor-profile, and country-risk products. Enforce data quality through Airbyte freshness validation macros, database constraints, and 35 dbt exposures documenting full data lineage across 7 product surfaces.
- Designed dimensional models for high-volume financial and time-series data, including a country-risk warehouse covering daily, weekly, monthly, quarterly, and annual macroeconomic indicators across 15+ African countries. Used star-schema patterns with surrogate keys and precomputed aggregates (revenue bands, EBITDA margins, transaction history), all materialized as indexed tables to eliminate runtime join latency and support millions of API reads with precision.
- Build and maintain 44 orchestrated Prefect 3.x pipelines processing end-to-end ELT workflows. Airbyte syncs ingest 61+ raw tables from operational sources into PostgreSQL, dbt transforms them into production-grade models, and downstream services (Qdrant semantic search, Algolia indexing, S3 documentation exports) consume the output. Implemented modular, reusable ELT patterns standardized across 20 domain modules with staged/production environment separation, enabling rapid onboarding of new data products without deep technical ramp-up.
- Built a reusable AI-driven data extraction subsystem using CrewAI and Claude that parses unstructured sources (PDFs, HTML, APIs) into Pydantic v2-validated datapoints across 19 specialized flows (CPI, inflation, FX, public debt, monetary policy). This enables Finance teams to receive structured, compliant financial data without manual processing.
- Led an end-to-end migration of the operational data layer from Airtable to NocoDB, rebuilding 199+ SQL models and re-architecting all Airbyte ingestion connections. This eliminated thousands in recurring tool costs while maintaining zero downtime for downstream financial data products
PythonELT/ETLPostgreSQLJavascriptdbtdata modellingCI/CDAWSDockerPrefectDBMLResearch Associate (Data Engineering)
Stears· Enterprise & SaaSAug 2022 — Dec 2023- Engineered an IoT data pipeline using MQTT and Python (Pandas, Polars, NumPy) to ingest real-time streaming sensor data and large-scale historical weather datasets, preparing inputs for MIRNet — a deep-learning temperature-forecasting model later published in the FUOYE Journal of Engineering and Technology.
- Developed preprocessing and feature engineering with Scikit-learn, optimizing data flow to an Nvidia GPU cluster for TensorFlow/Keras model training and supporting end-to-end data science workflows from ingestion through deployment.
PythonResearch WorkPandasPolarsMQTTData Streaming
Education
B.Sc. Electronics and Electrical Engineering
Obafemi Awolowo UniversityIle-Ife, Nigeria2017 — 2023
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