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Tier-1 employer and Hyderabad metro increase competition, but senior, specialized data skillset narrows the pool.
Data engineering skills transfer well, though healthcare governance/domain familiarity is advantageous.
Explicit 8+ years and mandatory Databricks/AWS/PySpark requirements create strict technical filters.
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Design, develop, and operate end-to-end data pipelines (batch and streaming) on AWS-based data lakehouse and warehouse platforms.
Build and maintain curated dimensional models and data quality controls to support BI and analytics with strong operational excellence including monitoring and incident triage.
Lead technical mentorship, set engineering standards, and collaborate with stakeholders to translate business needs into governed, reusable datasets.
8+ years of experience in Data Engineering with production-grade data platforms and pipelines.
Strong hands-on expertise with AWS data services (S3, Glue, Lambda, Step Functions, EMR/ECS/Fargate, CloudWatch) and Databricks Lakehouse on AWS.
Proficiency in Python, PySpark, Spark SQL, and complex SQL; experience with CI/CD, GitHub, Docker, and Infrastructure as Code (Terraform).
Bachelor’s degree in Computer Science, Engineering, or related field. No relocation or visa sponsorship; Hyderabad location; Hybrid work model.
Experienced in data warehousing and lakehouse architecture including dimensional modeling, SCDs, and performance optimization on large-scale datasets.
Strong orientation towards operational excellence with skills in data quality, monitoring, alerting, and root cause analysis in Agile teams.
Demonstrated leadership in mentoring engineers, defining technical standards, and influencing roadmaps and stakeholder communications on complex data engineering projects.