Professional Data Engineer

As a Professional Data Engineer, I specialize in making data usable and valuable for others by collecting, transforming, and publishing it. My expertise encompasses the entire lifecycle of robust data processing systems, including their design, construction, deployment, monitoring, maintenance, and security. I am adept at evaluating and selecting the most suitable products and services to meet specific business and regulatory requirements, ensuring that data solutions are both effective and compliant.

My capabilities extend to designing systems that prioritize security and compliance, incorporating elements like Identity and Access Management (e.g., Cloud IAM), data encryption, key management, and privacy considerations for Personally Identifiable Information using tools such as the Cloud Data Loss Prevention API, while also addressing data sovereignty and legal regulations. I ensure reliability and fidelity by preparing and cleaning data with tools like Dataprep or Dataflow, monitoring data pipelines, implementing disaster recovery and fault tolerance strategies, managing ACID compliance, and performing thorough data validation. Furthermore, I design for flexibility and portability, mapping business needs to architecture, and plan comprehensive data migrations, including to Google Cloud using services like BigQuery Data Transfer Service and Database Migration Service.

I am proficient in ingesting and processing data by planning and building efficient batch and streaming pipelines, leveraging services like Dataflow, Apache Beam, Dataproc, Cloud Data Fusion, BigQuery, and Pub/Sub. This includes data cleansing, transformation, and integration from diverse sources. I select optimal storage solutions, whether they involve Bigtable, Spanner, Cloud SQL, Cloud Storage, Firestore, or Memorystore, and plan for data warehouses, data lakes, or data mesh architectures using tools like Dataplex and Data Catalog. My skills also include preparing data for analysis and visualization, sharing data effectively through platforms like Analytics Hub, and maintaining and automating data workloads through resource optimization, repeatable job scheduling with tools like Cloud Composer, and diligent monitoring and troubleshooting to ensure system health and mitigate failures.