Data Engineer Sr. Manager

Established in 2008, Geidea epitomises 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 Thinking www.geidea.net To maintain competitive advantage as we grow, we are currently looking for Data Engineer Sr. Manager. Job purpose: Data Engineering leads the development, operation, and continuous improvement of the organization’s enterprise data platform on Google Cloud Platform (GCP). The role oversees the delivery of reliable, secure, and scalable data engineering solutions that support business intelligence, payments and operational reporting, regulatory requirements, and advanced analytics initiatives. It provides technical direction, manages engineering delivery and team performance, and ensures data platform capabilities align with business priorities, enterprise architecture, and applicable regulatory requirements. Key accountabilities and decision ownership: • Develop and manage the data engineering roadmap in line with the organization’s data strategy and business priorities, ensuring engineering initiatives support evolving business needs and align with enterprise architecture standards. • Oversee the design, development, and operation of the enterprise data platform on Google Cloud Platform (GCP), maintaining the reliability, availability, performance, and scalability of data services and pipelines, and ensuring timely resolution of incidents and recurring technical issues. • Lead the development and integration of batch and real-time data pipelines across payment and transaction systems, operational databases, application events, partner and banking feeds, third-party sources, SaaS platforms, and external APIs, ensuring data is processed reliably and made available to support business and analytical requirements. • Provide technical direction across the GCP data environment, including BigQuery, Dataflow, Pub/Sub, Dataproc, Cloud Composer, Cloud Storage, Datastream, Dataplex, and Data Catalog. Establish engineering standards, guide solution design and code reviews, and ensure the platform remains maintainable, scalable, and aligned with approved architectural principles. • Oversee the development of well-modelled, reusable data products that support business intelligence, management and regulatory reporting, risk and fraud analysis, and AI/ML initiatives, including integration with Vertex AI where required. • Lead and develop the data engineering team by setting objectives, managing workloads and resources, reviewing performance, and providing technical guidance and coaching to strengthen engineering capabilities and ensure delivery against agreed priorities. • Establish data quality and governance practices covering automated validation, data contracts, lineage, ownership, classification, retention, and access requirements, working with Data Governance and business stakeholders to maintain accurate, traceable, and trusted data. • Ensure the data platform incorporates appropriate security and privacy controls, including identity and access management, network restrictions, encryption, and data masking, in coordination with Information Security, Compliance, and Risk to support applicable requirements such as SAMA, PDPL, NDMO, and PCI DSS. Must have technical/professional qualifications: • Minimum of 8 years of relevant experience in data engineering, data platform development, or related technical roles. • At least 3 years of experience in a data engineering leadership or management role. • Bachelor’s degree in Computer Science, Information Technology, Software Engineering, or a related field. • Google Cloud Professional Data Engineer certification is preferred. • Proven experience designing, delivering, and operating production data platforms and pipelines on GCP. • Experience managing technical teams, engineering roadmaps, and platform operations in an enterprise environment. • Experience in fintech, payments, financial services, or other regulated industries is preferred. • Strong hands‑on experience with GCP (BigQuery, Dataflow, Pub/Sub, Composer). • Advanced SQL and strong Python programming skills. • Proven experience with batch and streaming pipelines (Beam, Spark, Kafka). • Solid knowledge of data warehousing, lakehouse, and data modelling. • Strong background in Airflow, dbt / Dataform, Terraform, and CI/CD. • Good understanding of data governance, security, and privacy frameworks. 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 solution Click apply below and become part of the Geidea story Show more Show less

Minimum of 8 years of relevant experience in data engineering, data platform development, or related technical roles. At least 3 years of experience in a data engineering leadership or management role. Bachelor’s degree in Computer Science, Information Technology, Software Engineering, or a related field. Google Cloud Professional Data Engineer certification is preferred. Proven experience designing, delivering, and operating production data platforms and pipelines on GCP. Experience managing technical teams, engineering roadmaps, and platform operations in an enterprise environment. Experience in fintech, payments, financial services, or other regulated industries is preferred. Strong hands‑on experience with GCP (BigQuery, Dataflow, Pub/Sub, Composer). Advanced SQL and strong Python programming skills. Proven experience with batch and streaming pipelines (Beam, Spark, Kafka). Solid knowledge of data warehousing, lakehouse, and data modelling. Strong background in Airflow, dbt / Dataform, Terraform, and CI/CD. Good understanding of data governance, security, and privacy frameworks.

Develop and manage the data engineering roadmap in line with the organization’s data strategy and business priorities, ensuring engineering initiatives support evolving business needs and align with enterprise architecture standards. Oversee the design, development, and operation of the enterprise data platform on Google Cloud Platform (GCP), maintaining the reliability, availability, performance, and scalability of data services and pipelines, and ensuring timely resolution of incidents and recurring technical issues. Lead the development and integration of batch and real-time data pipelines across payment and transaction systems, operational databases, application events, partner and banking feeds, third-party sources, SaaS platforms, and external APIs, ensuring data is processed reliably and made available to support business and analytical requirements. Provide technical direction across the GCP data environment, including BigQuery, Dataflow, Pub/Sub, Dataproc, Cloud Composer, Cloud Storage, Datastream, Dataplex, and Data Catalog. Establish engineering standards, guide solution design and code reviews, and ensure the platform remains maintainable, scalable, and aligned with approved architectural principles. Oversee the development of well-modelled, reusable data products that support business intelligence, management and regulatory reporting, risk and fraud analysis, and AI/ML initiatives, including integration with Vertex AI where required. Lead and develop the data engineering team by setting objectives, managing workloads and resources, reviewing performance, and providing technical guidance and coaching to strengthen engineering capabilities and ensure delivery against agreed priorities. Establish data quality and governance practices covering automated validation, data contracts, lineage, ownership, classification, retention, and access requirements, working with Data Governance and business stakeholders to maintain accurate, traceable, and trusted data. Ensure the data platform incorporates appropriate security and privacy controls, including identity and access management, network restrictions, encryption, and data masking, in coordination with Information Security, Compliance, and Risk to support applicable requirements such as SAMA, PDPL, NDMO, and PCI DSS.