Tamara is a leading fintech platform in Saudi Arabia and the GCC region with a mission to help people own their dreams built on being simple, trusted, and everywhere our customers need us. Today, more than 25 million customers and over 130,000 partner merchants depend on Tamara every day for reliable financial solutions across retail, travel, education, healthcare, and beyond. Tamara is backed by Sanabil Investments, a wholly owned company of the Public Investment Fund (PIF), and SNB Capital, among others.
Your Role
We are looking for a Senior Data Engineer to join the team building Tamara's next-generation data core - a multi-cloud data platform spanning Event Streaming, Data Warehousing, and Experimentation. You will be hands-on; designing and operating petabyte-scale streaming and batch systems that run across clouds. This role is ideal for engineers who love solving complex data challenges at scale, think with a platform mindset, and are excited to raise the technical bar of the team around them.
Your Responsibilities
Lead the design and build of platform components that run across multiple clouds — addressing portability, cross-cloud replication, HA/DR, and data residency requirements of a regulated fintech environment, and designing against single-cloud lock-in.
Build and operate large-scale event streaming infrastructure on Kafka, including configuration tuning and cross-cluster replication between cloud environments.
Build and maintain real-time and batch data pipelines using tools like Kafka, Spark, and Airflow.
Drive the development of a scalable LakeHouse architecture using modern open table formats such as Iceberg or Delta Lake.
Own reliable, cost-aware operation of multi-cloud data infrastructure — SLO-based observability, data quality, and lineage.
Enable Analytics and Product teams through self-serve data products, well-modeled warehouse foundations and marts, and reusable ingestion (inbound) and consumption (outbound) patterns.
Provide technical leadership to a group of 5-6 engineers and data engineers — leading design reviews, setting engineering standards, and driving knowledge sharing and documentation.
Preferably 6+ years of experience in data engineering or distributed backend systems, including 3**+ years operating large-scale streaming or data platform infrastructure in production**, and experience mentoring or technically leading engineers.
Hands-on experience with streaming platforms (e.g., Kafka) and distributed processing tools (e.g., Spark or Flink) — our event streaming platform is built on Kafka.
Hands-on experience building and operating data pipelines on GCP with BigQuery (and familiarity with multi-cloud patterns).
Working knowledge of LakeHouse formats (Iceberg/Delta/Hudi) and columnar storage like Parquet.
Experience with infrastructure-as-code and cloud-agnostic tooling (e.g., Terraform, Kubernetes) for deploying and operating data platforms across clouds.
Strong in data modeling and optimizing cloud data warehouses — we run on BigQuery and looking to expand to other Cloud DWHs.
Who You Are
A mentor and multiplier: passionate about driving engineering best practices and guiding a group of engineers to grow and deliver.
An owner: you build it, you run it — comfortable carrying operational responsibility for mission-critical streaming and data systems.
Comfortable with evolving scope and ambiguity as the platform grows from one cloud to many.
Compliance-aware: you treat security, data residency, and auditability as first-class design constraints in fintech.
Motivated by aligning data architecture with business outcomes, and by building platforms other teams love to use.
Nice to Have
Familiarity with observability tools like Prometheus, Grafana, and Loki to monitor and troubleshoot data systems.
Exposure to real-time OLAP engines like ClickHouse, Apache Druid, or Apache Pinot for low-latency analytics.
Prior experience in fintech or a highly regulated industry, with awareness of data residency, security, and compliance requirements.
Experience in Airflow or similar orchestration platforms.
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