Description

Job Description

• 6–8 years of experience designing, building, and maintaining scalable data and API-driven applications, with a strong focus on cloud-native architectures.
• Proven expertise working within enterprise data warehouse and analytics ecosystems, analyzing and modeling complex cross-object and cross-domain data relationships.
• Demonstrated success as a hands-on individual contributor, owning end-to-end delivery across 2–3+ full software engagements—from requirements and architecture to deployment and production support.
• Strong proficiency in SQL, Python, and Java for data transformation, pipeline orchestration, backend services, and automation.
• Advanced experience using Java for Dataflow (Apache Beam) pipelines, API development, and microservices, with a solid grasp of object-oriented design, performance tuning, and error handling.
• Deep hands-on knowledge of Google Cloud Platform data services, including:
• Big Query for large-scale analytics and optimized query performance
• Pub/Sub for event-driven and streaming architectures
• Cloud Storage (GCS) for durable, cost-effective data storage
• Cloud Functions for lightweight, event-based processing
• Dataflow (Apache Beam) for batch and streaming data pipelines
• Experience designing and operating batch, streaming, and near–real-time pipelines, ensuring scalability, fault tolerance, schema evolution, and cost efficiency.
• Strong command of data quality and reliability engineering, including data validation, reconciliation, monitoring, and exception-handling strategies aligned with production-grade requirements.
• Hands-on experience validating RESTful APIs, covering schema integrity, payload validation, downstream data correctness, and performance SLAs.
• Exposure to validating analytics and visualization layers using Looker Studio and Tableau, ensuring accuracy and consistency between source systems, transformations, and dashboards.
• Working knowledge of graph databases and their applicability for modeling highly connected datasets (nice to have).
• Strong data engineering mindset, with the ability to design, simulate, test, and optimize real-world data scenarios across GCP environments.
• Comfortable working within Agile/Scrum delivery models, collaborating closely with cloud architects, product owners, data scientists, and DevOps/SRE teams.
• Alignment with Google Cloud Data Engineer best practices, including security, IAM, cost optimization, monitoring, and operational excellence.

Key Skills

Education

Any Graduate