-Architect, build, and optimize data pipelines for batch, streaming, and event-driven data workflows using GCP services such as BigQuery, Dataflow, Pub/Sub, Cloud Storage, and Cloud Composer.

-Develop and maintain ETL/ELT frameworks that transform data from multiple sources into structured, query-ready formats.

-Work with structured, semi-structured, and unstructured data across diverse sources including APIs, files, logs, and databases.

-Implement data quality, lineage, and monitoring systems using Cloud Monitoring and alerting according to latest group strategies.

-Collaborate with Data Scientists, Analysts, and Product Managers to deliver scalable datasets supporting analytics and reporting.

-Ensure data security, governance, and compliance through IAM roles, VPC configurations, and best-in-class encryption practices.

-Contribute to architecture decisions that balance scalability, performance, and cost within GCP.

-Mentor junior engineers, enforce best practices in data engineering, and lead code review sessions.

-Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related field.

-5+ years of professional experience designing and operating data pipelines and platforms.

-Strong proficiency in SQL and Python for data processing and automation.

-Deep hands-on experience with GCP services (BigQuery, Dataflow, Cloud Storage, Pub/Sub, Dataproc)

-Experience in use of AI assist tooling for coding, test management.

-Experience managing data lake or warehouse infrastructure, integrating both structured and unstructured sources.

-Strong understanding of data modeling, partitioning, and schema design for analytical workloads.

-Excellent problem-solving and analytical thinking.

-Effective communication across technical and business teams.

-Leadership mindset with mentoring and cross-functional collaboration experience.

-Proven ability to manage and coordinate across multiple domains

What additional skills will be good to have?

-Familiarity with industry best practices for engineering maturity, standards, and infrastructure components.

-Experience as data engineering lead in large-scale projects, integrating ML pipelines or AI workflows on multiple cloud platforms.

-Certification: Google Cloud Professional Data Engineer or relevant Data Engineering or AI Certifications (preferred).