Skillset : Data Architect - Data Engineering, Distributed Systems, GCP Cloud Architecture

Experience Level : 8+ Years

Required Skills :

- Strong experience in designing and architecting large-scale data platforms andistributed data processing systems.
- Solid programming experience in Python, with hands-on experience in developing data processing and ETL/ELT pipelines.
- Hands-on experience with Apache Spark/PySpark, Kafka, and distributed data processing.
- Strong understanding of data architecture, data modeling, data integration, ETL/ELT pipelines, and data warehousing.
- Strong knowledge of SQL and NoSQL databases and experience in designing scalable data solutions.
- Strong hands-on experience with GCP cloud services and architecture, including BigQuery, GCS, Pub/Sub, Dataflow, Dataproc, Cloud Run, and Workflows.
- Strong understanding of distributed systems, event-driven architecture, scalability, high availability, and fault-tolerant system design.
- Experience with Docker, Kubernetes, and CI/CD practices.




- Experience in performance tuning and optimization of data pipelines and cloud-based data platforms.
- Strong understanding of cloud security, data governance, access management, and best practices on GCP.
- Excellent problem-solving, stakeholder management, technical leadership, and architecture decision-making skills.

Good-to-Have Skills :

- Experience with Apache Flink, Airflow, Databricks, or Snowflake.
- Experience with Terraform or other Infrastructure as Code (IaC) tools.
- Exposure to AI/ML data pipelines, GenAI, or Agentic AI solutions.
- Experience with observability and monitoring tools such as Cloud Monitoring, Prometheus, Grafana, ELK, or Splunk.
- GCP Professional Cloud Architect or Professional Data Engineer certification.
- Experience in mentoring teams and driving architecture standards, best practices, and governance.

📌 NITOR - Data Architect - ETL Pipeline (India)
🏢 Nitor Infotech, an Ascendion
📍 India