Your responsibilities will include Builds Data Pipelines prioritized by D&A (with support of DE lead and Data product owner); Performs and documents Unit Tests for Data Pipelines; Supports Data Product Owners (BI Teams and Data Science Team) on UAT Tests; Coordinates Data Pipeline deployment (creates pull Request, requests Business approval, creates cutover plan, loads historical data, coordinates deployment with vendors and requests schedules); Hands-on experience building and maintaining batch and streaming data pipelines using Dataflow, Pub/Sub, and Cloud Storage, following established production patterns. Strong proficiency in BigQuery, including partitioning, clustering, basic performance optimization, and cost-aware design under guidance. Experience with workflow orchestration using Cloud Composer (Airflow), including task dependencies, retries, and failure handling. Proficiency in Python for data processing, with experience writing unit tests using frameworks such as pytest and applying data quality validation checks. Good understanding of analytical data modeling, including Star Schema concepts and Medallion Architecture (Bronze/Silver/Gold). Solid software engineering fundamentals, including Git-based workflows, participation in code reviews, and basic experience with Docker and CI/CD pipelines (e.g., Cloud Build). Working knowledge of GCP fundamentals, including IAM, service accounts, and basic VPC and network security concepts. Exposure to enterprise cloud platforms beyond GCP (e.g., AWS or Azure) or hybrid data environments. Familiarity with reference architectures, data product concepts, and domain-oriented data platforms. Basic knowledge of SAP systems (ECC, BW, or S/4) and enterprise source system integration. Exposure to enterprise integration tools such as Informatica or WSO2 (hands-on or project-level). Awareness of data security, compliance, and governance practices, including access controls and data sensitivity. Experience participating in knowledge transfer (KT) activities and supporting operational or sustain teams. Strong communication skills, with the ability to collaborate effectively with BI, Data Science, and platform teams. Exposure to data reliability concepts (logging, monitoring, retries, idempotency), schema evolution, and early experience with GenAI-assisted development tools (e.g., Cursor, VS Code Cline) or Vertex AI use cases.
