Data Engineers

Role Overview We are seeking a highly skilled Senior Data Engineer / ML Engineer with strong expertise in building scalable ELT/ETL pipelines, cloud-native data architectures, and API-driven integrations. The ideal candidate will have hands-on experience in Google Cloud Platform (GCP), Data Vault modeling, and the ability to translate complex business logic into robust engineering pipelines and data models. Key Responsibilities Design, develop, and maintain scalable ELT/ETL data pipelines on GCP. Build and optimize data engineering pipelines for structured, semi-structured, and streaming data. Implement and manage ML engineering workflows for production deployment. Develop and maintain REST/SOAP APIs for system integrations. Architect and implement Data Vault models for enterprise data warehousing. Design event-driven architectures using Pub/Sub and streaming frameworks. Integrate third-party systems into enterprise data ecosystems. Collaborate with business stakeholders to translate business requirements into scalable data models and pipelines. Ensure adherence to security best practices (IAM, VPC Service Controls, encryption, service accounts). Optimize SQL queries and data performance in BigQuery. Support reporting and analytics solutions (Power BI preferred). Required Skills & Expertise Strong experience building scalable ELT/ETL pipelines, ML engineering workflows, and API integrations (REST/SOAP). Hands-on expertise within the GCP ecosystem: Dataform, Cloud Run, Pub/Sub, BigQuery (advanced SQL), GCS, and Firestore. Solid understanding of Data Vault modeling and enterprise data architecture principles. Experience designing event-driven and streaming architectures (Kafka, Pub/Sub). Working knowledge of GKE, Spark, and Java-based data processing solutions. Familiarity with MCP integration and TMF protocols. Strong ability to translate business requirements into scalable data models and engineering pipelines. Experience implementing cloud security best practices including IAM, VPC Service Controls, service accounts, and encryption. Exposure to Power BI for reporting and analytics is an added advantage.