Data Infrastructure Engineer (Big Data)

Backend Engineer -Data

Key Responsibilities

Big Data Processing:

Implement and manage big data processing systems. Experience or strong interest in big data implementation is required.Data Pipeline Implementation:

Develop and maintain robust data processing pipelines. Candidates should have experience or a strong interest in data pipeline architectures.Batch vs. Real-Time Processing:

Clearly articulate the differences in implementation strategies between batch processing and real-time APIs.Streaming Data Responsibilities:

Explain the separation of responsibilities between producers and consumers in streaming data processes.Database Management:

Build and operate databases storing over 10 GiB of data, ensuring efficiency and scalability.Data Platform Operations:

Operate platforms such as Amazon Redshift, Google BigQuery, Snowflake, and Databricks. Experience in managing these or similar platforms is highly desirable.Qualifications and Skills

Experience with Big Data:

Proven track record in handling large-scale data projects, with specific skills in time-series databases, streaming data processing, and multi-tiered database architectures.Data Warehousing and Data Lakes:

Hands-on experience with data warehouse and data lake technologies, including understanding of Lambda architecture.Technical Proficiency:

Strong technical skills in relevant big data technologies and frameworks.Problem Solving:

Excellent analytical and problem-solving skills, capable of managing complex data challenges.Communication:

Effective communication skills, able to document and explain data processes clearly to both technical and non-technical stakeholders.Nice to Have

Cloud Experience:

Experience with cloud platforms such as AWS, Google Cloud Platform (GCP), or Microsoft Azure.Knowledge of cloud-based data storage solutions (e.g., S3, Google Cloud Storage, Azure Blob Storage).Familiarity with cloud-based data processing services (e.g., AWS Lambda, Google Cloud Dataflow, Azure Data Factory).Experience with cloud infrastructure automation and management tools (e.g., Terraform, CloudFormation, Ansible).

Machine Learning Integration:

Understanding of integrating machine learning models into data pipelines.DevOps Practices:

Experience with DevOps practices and tools for continuous integration and deployment (CI/CD).Data Security:

Knowledge of data security best practices and compliance standards in cloud environments.Visualization Tools:

Experience with data visualization tools and platforms (e.g., Tableau, Power BI, Looker).Programming Languages:

Proficiency in additional programming languages relevant to data processing and backend development (e.g., Scala, Go, Rust).

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