Established in 2008, Geidea epitomizes customer focused empowerment and commercial success through continuous innovation. Geidea makes best in class digital payment solutions available for all by attracting and leveraging the best creative & entrepreneurial talent in the market Our solutions give any business the chance to get ahead and reach for more no matter their size or maturity. Our technology mirrors our people - Smart, Innovative & Forward Thinkingwww.geidea.net

To maintain a competitive advantage as we grow, we are currently looking for a new " Senior Data Engineer"
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Job purpose: to maintain a competitive advantage as we grow, we are looking for a highly skilled Senior Data Engineer to design, develop, and optimize scalable, secure, and high-performance data platforms across the enterprise. This role will play a key part in building and maintaining the data ecosystem that supports our Fintech services, including real-time financial transactions, credit scoring models, regulatory reporting, and customer analytics. The Senior Data Engineer will work closely with Data Architects, Software Engineers, Product Teams, and Data Analysts to develop modern data solutions leveraging Big Data technologies, Data Lakes, ELT/ETL pipelines, and Cloud Data Warehouses while ensuring reliability, security, governance, and regulatory complianc
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Responsibilities:Data Engineering & Platform Development Design, build, and maintain scalable data pipelines for batch and real-time data processing. Develop and optimize data ingestion frameworks from internal and external data sources. Implement and maintain data models to support analytics, reporting, and operational use cases. Collaborate with Data Architects to translate architectural designs into production-ready solutions. Big Data & Distributed Processing Develop and maintain large-scale data processing solutions using technologies such as Apache Spark, Databricks, Flink, Trino, or Presto. Build and optimize distributed data processing workloads handling high-volume financial and behavioral datasets. Work with distributed storage systems including S3, ADLS, HDFS, and related cloud-native services. Optimize data formats such as Parquet, ORC, and Avro for performance and storage efficiency. Data Lakes & Lakehouse Solutions Build and maintain Data Lake and Lakehouse environments using technologies such as Delta Lake, Apache Hudi, or Apache Iceberg. Implement data quality, partitioning, schema evolution, and lifecycle management processes. Support data governance and metadata management initiatives across all data layers. ELT/ETL Development Design, develop, and support robust ELT/ETL pipelines using tools such as Apache Airflow, DBT, AWS Glue, Azure Data Factory, or Kafka Connect. Develop reusable and maintainable transformation logic using SQL, Python, or Scala. Ensure pipeline reliability through monitoring, alerting, logging, and automated recovery mechanisms. Optimize data processing performance and cost efficiency. Data Warehousing & Analytics Enablement Develop and maintain cloud-based data warehouse solutions such as Snowflake, Redshift, Big Query, or Synapse Analytics. Build and optimize dimensional models, fact tables, and data marts to support business intelligence and reporting requirements. Collaborate with analytics teams to ensure efficient access to trusted and governed data assets. Support integration with BI platforms such as Power BI, Tableau, and Looker. Security, Governance & Compliance Implement data security controls including encryption, masking, tokenization, and access management. Ensure compliance with SAMA, NCA, GDPR, and internal security policies. Support data lineage, auditing, and governance initiatives through integration with metadata and cataloging solutions. Participate in data quality and governance programs to ensure accuracy and consistency of enterprise data. Dev Ops & Observability Contribute to CI/CD pipelines and Infrastructure as Code implementations using Terraform, Cloud Formation, or similar tools. Implement monitoring and observability solutions for data pipelines and platforms. Establish and maintain SLAs, data quality checks, and operational dashboards using tools such as Grafana, Prometheus, or Datadog. Troubleshoot production issues and provide performance tuning recommendatio
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Qualifications:Bachelor's or Master's degree in Computer Science, Data Engineering, Information Systems, or a related technical field.5+ years of experience in Data Engineering, with hands-on experience building and supporting production-grade data platforms. Strong experience with Big Data technologies such as Apache Spark, Databricks, Flink, or similar distributed processing frameworks. Proven experience building and maintaining Data Lakes, Lakehouse architectures, and cloud-based data platforms. Strong proficiency in SQL and at least one programming language such as Python, Scala, or Java. Experience developing ELT/ETL pipelines using Airflow, DBT, AWS Glue, Azure Data Factory, or equivalent tools. Hands-on experience with cloud platforms such as AWS, Azure, or GCP. Experience with cloud data warehouses including Snowflake, Redshift, Big Query, or Synapse. Understanding of data governance, security, and regulatory requirements within Fintech, Banking, or highly regulated environments. Experience working with CI/CD, Infrastructure as Code, and monitoring tools is highly prefer
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Our values guide how we think and act - They describe what we care about the most Customer first - It’s embedded in our design thinking and customer service approach Open - Openness allows us to constantly improve and evolve Real - No jargon and no excuses! Bold - Constantly challenging ourselves and our way of thinking. Resilient – If we fail, we bounce back stronger than before. Collaborative - We know that we can achieve a lot more as a team. We are changing lives by constantly striving for a better solu
tion.