About the Company
SPAN Digital is a premium software consultancy partnering with startups and global brands in tech, fintech, and digital media. The company builds category-defining products and helps engineering teams deliver exceptional digital solutions across three continents. SPAN balances structure with flexibility, autonomy with accountability, and learning with doing.
About the Role
The Data Engineer will contribute to enterprise-scale data retention, migration, and privacy enhancement projects at petabyte scale. This role involves building production-quality data solutions, collaborating with senior engineers, and gaining hands-on experience with complex data engineering initiatives.
Key responsibilities
- Contribute to large-scale data migration initiatives for identity transformation across multi-petabyte datasets
- Build and maintain data transformation pipelines for batch and streaming processing
- Implement data validation and quality checks to ensure migration accuracy and data integrity
- Support JDK and Apache Spark upgrades while maintaining production stability
- Develop monitoring solutions to track data pipeline performance and system health
- Create automated testing for data migration scenarios
- Document data lineage, dependencies, and transformation logic
- Participate in code reviews and technical design discussions
- Collaborate with cross-functional teams (product, engineering, operations) to deliver data solutions
- Take ownership of well-defined components within larger projects
- Contribute to team knowledge sharing and documentation efforts
- Continuously learn and apply modern data engineering practices and technologies
Required
- 3-5 years in data engineering, software engineering, or related technical roles
- Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience
- Solid experience in ETL pipeline development and data transformation
- Strong programming skills in Scala and/or Java with modern build systems (Gradle or Maven)
- Hands-on experience with Apache Spark for distributed data processing
- Familiarity with distributed file systems (HDFS) or cloud object storage (S3, GCS)
- Experience with at least one cloud data platform (AWS, GCP, Azure, or Databricks)
- Understanding of data structures, algorithms, and distributed systems concepts
- Strong problem-solving abilities and attention to detail
- Excellent communication skills for technical collaboration
Preferred Qualifications
- AWS experience, particularly with EKS (Kubernetes) for containerized applications
- Exposure to data privacy and compliance requirements (GDPR, CCPA)
- Experience with modern data formats: Apache Iceberg, Parquet, ORC, Avro
- Familiarity with streaming platforms: Apache Kafka or AWS Kinesis
- Knowledge of observability tools: Prometheus, Grafana, Spark UI
- Experience with data lineage analysis and validation frameworks
- Prior work in enterprise engineering environments with large-scale systems
Benefits
- Work on cutting-edge data engineering projects at petabyte scale
- Mentorship and learning from experienced engineers
- Collaborative culture focused on continuous growth
- Exposure to enterprise clients in entertainment and technology industries
- Clear career progression paths with defined leveling framework
- Flexible work arrangements across global offices
- Competitive compensation package
- Access to latest technologies and tools in data engineering and cloud platforms