Senior Data Engineer
tao digital solutions
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Job Description
Hi,
We are currently seeking Senior AWS Data Engineer position @ TAO Digital Solutions.
We are seeking a Senior AWS Data Engineer to implement, operate, and continuously optimize a cloud-native OLAP and Lakehouse platform on AWS. While core architecture patterns are already defined, this role is responsible for correct implementation, operational excellence, observability, and cost-efficient operation of the data platform. The platform supports customer-facing analytics, self-service reporting, and future AI/ML workloads using Amazon Redshift, Apache Iceberg on S3, PySpark, and AWS-native services.
Key Responsibilities
• Implement and operate AWS-native OLAP and Lakehouse architecture (Redshift, S3, Iceberg)
• Build and operate Aurora MySQL data migration, replication, and CDC pipelines
• Develop and maintain PySpark-based ETL pipelines
• Implement Redshift ETL, views, and materialized views
• Actively optimize query performance and cost in Redshift and Iceberg
• Manage Iceberg tables, MERGE logic, partitioning, and schema evolution
• Orchestrate data pipelines using Airflow and/or AWS Step Functions
• Implement observability, data quality checks, alerts, and operational dashboards
• Enforce PHI masking, tenant isolation, and query guardrails
• Continuously optimize storage, compute usage, and query cost efficiency
Required Qualifications
• 10+ years of hands-on experience in Data Engineering
• Strong experience with Amazon Redshift, S3, and AWS Glue Data Catalog
• Mandatory experience with Apache Iceberg (MERGE, partitioning, schema evolution)
• Mandatory experience with PySpark for large-scale data transformations
• Experience with Aurora MySQL migration, replication, and CDC pipelines
• Hands-on experience with Airflow and/or AWS Step Functions
• Experience developing AWS Lambda-based data workflows
• Infrastructure-as-code experience using Terraform
• Advanced SQL and strong Python data engineering skills
• Strong experience optimizing analytics cost and query efficiency
What Success Looks Like
• Analytics workloads are reliable, observable, and cost-efficient
• Query costs in Redshift and Iceberg are predictable and well-controlled
• Data freshness, security, and tenant isolation are enforced by design
• The platform is stable, scalable, and ready for AI-native workloads
Interested candidates please forward your updated resume to the following email ID ([HIDDEN TEXT])
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