The role involves sourcing data from multiple systems, optimizing workflows and collaborating with architects and analysts to deliver clean, well-structured datasets.
Required Skills & Experience:
• 3+ years of experience in data engineering across hybrid environments (on-premise and cloud).
• Proficiency in SQL and Python or Java/Scala.
• Hands-on experience with ETL/ELT tools and frameworks.
• Good understanding of GCP data services: BigQuery, Dataproc, Dataflow, Cloud Storage.
• Familiarity with data modeling, schema design, and metadata management.
• Knowledge of data governance, security, and compliance best practices.
Nice To Have:
• GCP certification (e.g., Professional Data Engineer) is a major plus.
• Experience with Big Data technologies.
Key Responsibilities:
• Solution Design: Architect data pipelines down to the low-level elements, ensuring clarity and precision in implementation.
• Data Sourcing: Extract data from diverse repositories including relational
databases (Oracle, PostgreSQL), NoSQL stores, file systems, and other
structured/unstructured sources.
• Data Transformation: Design and implement ETL/ELT workflows to standardize and cleanse data using best practices in data engineering.
• Pipeline Development: Build scalable, fault-tolerant data pipelines that support batch and streaming use cases.
• Cloud data processing: Load transformed data into GCP destinations such as BigQuery or Cloud Storage using tools like Dataproc, Dataflow, and other GCPnative services.
• Workflow Orchestration: Design and manage workflows using orchestration tools such as Apache Airflow or Cloud Composer.
• Data Format Expertise: Work with various data formats including JSON, AVRO, Parquet, CSV, and others.
• Optimization & Monitoring: Ensure performance, reliability, and cost-efficiency of data pipelines through continuous monitoring and tuning.
• Collaboration: Work closely with data architects, analysts, and business
stakeholders to understand data requirements and deliver high-quality solutions.