Data Engineering Fabric
EXL
This role involves working with Spark notebooks and building data ingestion pipelines. Familiarity with Delta Lake and lakehouse architecture is required.
Last checked on September 24, 2026. We may earn a commission when you click through.
This role is ideal for those passionate about data engineering and eager to work with advanced technologies. However, the lack of disclosed salary might deter some candidates.
About this role
This role involves working with Spark notebooks and building data ingestion pipelines. Familiarity with Delta Lake and lakehouse architecture is required.
About the Company
EXL is a leading analytics and digital transformation services company, providing insights and solutions across various industries.
Key Highlights
- ✓ Experience with Spark notebooks
- ✓ Building ingestion pipelines from diverse sources
- ✓ Knowledge of Delta Lake and medallion architecture
- ✓ Focus on data engineering best practices
💡 Honest Take: Getting into data engineering at EXL can be rewarding, but be prepared for a challenging onboarding process. The workload can be heavy, especially when integrating multiple data sources.
Pros
- ✓ Opportunity to work with cutting-edge technologies
- ✓ Strong company reputation in the analytics field
- ✓ Chance to build complex data pipelines
Cons
- ✗ No salary information provided
- ✗ Potentially high workload
- ✗ Non-remote position may limit applicant pool
Best For: Professionals seeking to deepen their skills in data ingestion and lakehouse architecture while working in a structured company.
Watch Out: The position is non-remote, which may not suit candidates seeking flexibility in their work environment.
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Expert Review
The position at EXL highlights the importance of practical experience with Spark notebooks and Delta Lake, making it suitable for engineers eager to enhance their skills. The focus on building ingestion pipelines from diverse sources indicates a dynamic working environment. While the job offers a chance to work with advanced technologies, the absence of salary transparency is a back that could affect candidate interest.
Our editorial team noted that potential applicants should be prepared for a high workload, particularly during the integration of various data sources. This role demands a proactive approach to problem-solving and adaptability. Candidates should also be aware that the non-remote nature of the position may limit its appeal for those preferring flexible work arrangements.
Ultimately, the position is data engineers ready to tackle complex challenges without the immediate knowledge of their compensation. Those looking for a clear salary structure might want to explore other opportunities before committing.
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