Data Infrastructure Optimization Expert
2 days ago
Key Responsibilities
- Design and Development: Architect and implement scalable Data Lakehouse Platforms using open-source technologies.
- Pipeline Optimization: Develop and optimize data pipelines for both real-time and batch processing with tools like Spark and Airflow.
- Storage and Format Management: Implement modern storage formats such as Apache Iceberg, Hudi, or Delta Lake, ensuring efficient data storage and retrieval.
- Orchestration and Deployment: Manage deployment and orchestration using Kubernetes and Docker across cloud (AWS, GCP, Azure) or on-prem environments.
- Data Governance: Establish and maintain robust governance practices, including database CDC replication and lineage tracking.
- Analytics Integration: Support interactive dashboards and analytics tools, such as Superset or similar, enabling data-driven decisions.
- Performance Tuning: Monitor system performance and implement optimizations to ensure scalability and cost-efficiency.
- Innovation: Stay ahead of industry trends, experimenting with emerging technologies to continuously improve the platform.
Required Skills and Qualifications
- 3+ years of experience in data platform engineering, with expertise in open-source tools and cloud technologies.
- Strong understanding of distributed computing frameworks like Spark and query engines like Trino or Presto.
- Proficiency in containerization and orchestration technologies (Docker, Kubernetes).
- Hands-on experience with cloud platforms (AWS, GCP, Azure) and hybrid/on-prem infrastructure.
- Familiarity with ETL/ELT workflows, CDC replication tools, and modern data storage solutions.
- Proficiency in programming languages like Python, Java, or Scala.
- Experience managing large-scale data systems (terabyte/petabyte-scale) and optimizing for performance.
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