Data Engineer

Open to full-time
Bio

Zainab O. is a Data Engineer with several years of experience in designing and implementing scalable data pipelines and analytics solutions. She utilizes technologies such as Python, SQL, Apache Airflow, Docker, AWS, and Terraform to build robust ETL and ELT pipelines, automate workflows, and deliver reliable datasets for reporting and business decision-making. Zainab is adept at using Airbyte and Redshift for data movement and analytics, and she ensures infrastructure consistency with Terraform. Her work often involves integrating structured and unstructured data, developing data warehouses, and generating dashboards. At Guaranty Trust Holding Company, Zainab engineered automated data pipelines that reduced manual intervention by 70%, improving the timeliness of operational and regulatory reports. She also developed a reliable data ingestion pipeline using Apache NiFi and Airflow for a core banking environment, enhancing data availability for reporting and analytics. Zainab holds a Bachelor of Science in Statistics from the University of Ilorin. She is best suited for roles that involve building and optimizing data pipelines, ensuring data reliability, and supporting analytics and reporting functions.

Expertise
PythonSQLAWSRelational DatabaseSnowflake
Video Intro
Assessments
APPLICANT AVG · 46%ZAINAB · 75%0255075100TopASSESSMENT SCORE (0–100)

General Project Assessment

(75/100)
  • Great on Implement LRU Cache AlgorithmMedium
  • Great on Design an ETL Pipeline for Integrated InsightsHard
  • Great on Optimize AWS S3 Storage and Processing ArchitectureHard
Projects

Reliable API Data Ingestion Pipeline on AWS with Airflow

Project Description: As part of my work integrating external data into internal reporting systems, I worked on a pipeline to ingest data from a third-party API into our AWS environment. The business relied on this data for analysis and reporting, but access was inconsistent and sometimes handled manually, which created gaps in data availability and made historical tracking difficult. To improve this, I built a pipeline that automates the extraction of data from the API using Python and stores it in Amazon S3 as part of a raw data layer. I used Apache Airflow to orchestrate the workflow, managing scheduling, retries, and task dependencies to ensure the pipeline runs reliably. I also used Terraform to provision the S3 bucket and related infrastructure, making the setup easy to reproduce and maintain across environments. The focus throughout was on keeping the pipeline simple, but dependable enough for downstream reporting and analytics use. Technologies Used: Python, Apache Airflow, AWS S3, Terraform, SQL Key Achievements: Replaced manual and inconsistent data retrieval with an automated ingestion pipeline Improved reliability of data delivery through scheduling, retry logic, and monitoring in Airflow Established a centralized raw data layer in S3, making data easier to track and reuse Ensured consistent infrastructure setup using Terraform, reducing manual configuration effort Increased confidence in externally sourced data used by downstream reporting teams

Data Pipeline for Football Competition Data

Project Description: As part of improving how externally sourced data is integrated into internal systems, I worked on a pipeline to ingest and structure football competition data from a third-party API for analytics and reporting purposes. The data was required for downstream analysis, but there was no consistent way of capturing and storing it internally. This created gaps in availability and made it difficult to work with historical data. To address this, I implemented an end-to-end pipeline that extracts data from the API using Python and orchestrates the workflow with Apache Airflow. The data is first loaded into a PostgreSQL database on AWS RDS, which serves as a staging layer to ensure structure and control before further processing. From there, I used Airbyte to replicate the data into Amazon Redshift, making it accessible for querying and analysis. The setup was containerized using Docker, with Airbyte running on a local Kubernetes environment (Minikube), allowing for a consistent and reproducible deployment. The focus of the implementation was on reliability, clarity of data flow, and maintaining a clean separation between ingestion, staging, and analytics layers. Technologies Used: Python, Apache Airflow, PostgreSQL (AWS RDS), Airbyte, Docker, Minikube (Kubernetes), Amazon Redshift Key Achievements: Delivered a structured pipeline for integrating external API data into the analytics environment Improved data availability by replacing inconsistent access patterns with an automated workflow Established a staging layer (RDS) to improve data organization and control before warehousing Enabled seamless data replication into Redshift for downstream reporting and analysis Implemented a reproducible and maintainable setup using containerization and infrastructure tooling

Transaction Data Pipeline to Enterprise Data Warehouse

Project Description: As part of improving data availability for reporting and analytics within a core banking environment, I worked on a pipeline responsible for moving transaction data from the core banking application into the enterprise data warehouse (Oracle Exadata). The business relied heavily on transaction data for regulatory reporting, reconciliation, and operational insights, but the data flow into the warehouse needed to be more structured and reliable. To address this, I implemented a data pipeline using Apache NiFi to handle the ingestion and movement of transaction data from the source system into the data warehouse. NiFi was used to manage data flow, transformation, and routing, ensuring that data was transferred efficiently and in a controlled manner. Apache Airflow was used to orchestrate the overall process, managing scheduling, dependencies, and execution flow. This ensured that data ingestion jobs ran consistently and could be monitored effectively. The solution was deployed within an enterprise server environment, with a focus on reliability, traceability, and maintaining data integrity throughout the pipeline. Once ingested into Oracle Exadata, the data became available for downstream users, enabling reporting teams and analysts to build dashboards and derive insights from accurate and up-to-date transaction data. Technologies Used: Python, Apache NiFi, Apache Airflow, Oracle Exadata, SQL Key Achievements: Delivered a reliable pipeline for moving transaction data from core banking systems into the enterprise data warehouse Improved data availability for reporting, reconciliation, and analytics use cases Automated data ingestion and orchestration, reducing manual intervention and operational overhead Ensured consistent and traceable data flow using NiFi and Airflow Enabled downstream teams to access structured and timely transaction data for decision-making

Experience

  1. Data Engineer

    GURANTY TRUST HOLDING COMPANY (GTCO)· FinTechMay 2023 — Present
    • Engineers automated data pipelines using Python, SQL, and Apache Airflow (Dockerized), reducing manual intervention by 70% and ensuring timely delivery of operational and regulatory reports.
    • Orchestrates data movement from APIs and RDS to Redshift via Airbyte, enabling scalable analytics and improving query performance for cross-divisional reporting.
    • Implemented data validation checks and schema enforcement in Airflow pipelines, improving data reliability and reducing downstream reporting errors.
    • Designs a dimensional data warehouse (fact and dimension tables) in Redshift to support sales and marketing analytics.
    • Builds pipelines integrating customer, transactional, and CRM-related data for analytics and reporting.
    • Develop regulatory compliance reports that ensure adherence to Central Bank of Nigeria (CBN) requirements, directly supporting audit readiness and reducing compliance risk.
    • Builds and optimize Power BI dashboards and SSRS reports, providing executives with actionable insights that improved decision-making in marketing, compliance, and system control teams.
    • Develop dashboards tracking customer acquisition, campaign performance, and operational KPIs.
    • Partnered with business stakeholders to define revenue, customer, and performance metrics.
    • Design and maintain complex SQL queries, stored procedures, and views to handle datasets exceeding millions of rows, improving reporting efficiency and scalability.
    • Automates third-party customer data uploads into CRM platforms, reducing processing time by 50%.
    • Produce technical documentation (README, data flow diagrams, pipeline architecture) to streamline onboarding and knowledge sharing within the engineering team.
    PythonSQLApache AirflowDockerAWSRedshiftAirbytePower BISSRSSnowflake

  2. Business Intelligence Developer

    BLUECHIP TECHNOLOGIES LIMITED· OtherFeb 2022 — Mar 2023
    • Developed data ingestion workflows to extract data from various sources such as Microsoft Excel and Oracle SQL into a Hive data warehouse for analytical processing.
    • Integrated ERP and operational data into warehouse for sales reporting.
    • Summarized and aggregated millions of records using HiveQL (HQL) to simplify data and effectively communicate business performance insights.
    • Generated reports and responded to business data requests, transforming business data into actionable insights to support quality decision-making.
    • Automated reporting for customer performance analytics.
    • Developed a paginated sales invoice report using Power BI Report Builder, presenting data in a well-structured tabular format with advanced formatting for clarity and readability.
    • Built predictive model for customer response to marketing campaigns.
    • Adopted Oracle Analytics Server (OAS) to automate report generation and schedule report distribution to recipients at specified times.
    Microsoft ExcelOracle SQLHiveQLPower BIOracle Analytics Server (OAS)

  3. Data Analyst

    NIGERIA FOOTBALL FEDERATION (NFF)Oct 2019 — Jul 2022
    • Analyzed and managed financial records for cooperative members.
    • Supported administrative operations and data management.
    • Managed cooperative accounts and generated routine reports.
    • Handled all other cooperative related affairs.
    Microsoft ExcelMicrosoft PowerBI
Education

  1. Bachelor of Science, Statistics (Second Class Upper)

    University of Ilorin, IlorinIlorin2014 — 2018
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