Senior Data Engineer (Spark)

SSC HR Solutions

Description Builds and runs large scale batch data pipelines, keeping jobs fast and affordable as data volumes grow. Works with Apache Spark to build and manage large scale data processing jobs, including performance tuning to maintain efficient and reliable pipeline execution.

Posted 9/20/2026full-timeRemote • EgyptSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Expertise in building and managing large scale batch data pipelines using Apache Spark, with a focus on performance tuning, data modelling for analytics, and ensuring data quality through automated processes. Proficient in collaborating with platform and product teams to facilitate end-to-end data flows and utilizing open table formats like Apache Iceberg.

Highest-signal resume keywords

Apache Spark Performance TuningData Modelling for AnalyticsAutomated Scheduling and MonitoringApache Iceberg or Open Table FormatsTrino or Similar Query Engines

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Apache SparkData ModellingPerformance TuningAutomated SchedulingData Quality ChecksApache IcebergTrinoBatch Data PipelinesEnd to End Data FlowsLakehouse Architecture

Tools & Technologies

Apache SparkTrinoApache Iceberg

Industry Keywords

Data PipelinesData ProcessingData QualityAnalyticsLakehouse

Tech Stack

Tools & technologies

ApacheSpark

About the role

Key responsibilities & impact

Description
Builds and runs large scale batch data pipelines, keeping jobs fast and affordable as data volumes grow. Works with Apache Spark to build and manage large scale data processing jobs, including performance tuning to maintain efficient and reliable pipeline execution. Supports data modelling for analytics and organises data in a lakehouse so it is usable downstream. Handles automated scheduling, monitoring, and data quality checks, while working with platform and product teams on end to end data flows.

Requirements

What you’ll need

Requirements
- Strong hands on Apache Spark including performance tuning, not Spark usage through a managed notebook only.
- Data modelling for analytics and organising data in a lakehouse so it is usable downstream.
- Automated scheduling, monitoring, and data quality checks.
- Works with platform and product teams on end to end data flows.
- Apache Iceberg or other open table formats.
- Trino or similar query engines.
- On premises or self managed cluster experience.