Key Responsibilities :

- Define end-to-end GCP data architecture across data ingestion, storage, processing, analytics, and governance.

- Design scalable lakehouse and data warehouse solutions using BigQuery, Cloud Storage, BigLake, and Dataplex.

- Architect batch and streaming data pipelines using Dataflow, Dataproc, Pub/Sub, and Datastream.

- Lead conceptual, logical, and physical data modeling using dimensional modeling, Data Vault, and medallion architecture.

- Lead migration of legacy data platforms from Teradata, Oracle, Hadoop, Snowflake, or Redshift to BigQuery.

- Establish standards for orchestration, CI/CD, infrastructure as code, and code quality.

- Define data governance, lineage, cataloging, data quality, security, and compliance frameworks.

- Optimize BigQuery performance and cost through partitioning, clustering, materialized views, and slot management.

- Enable analytics and AI/ML use cases using Looker, Vertex AI, and BigQuery ML.

- Lead architecture reviews, mentor data engineers, and collaborate with clients and business stakeholders on solution design and roadmaps.

Required Skillsets:

- Strong hands-on expertise in GCP data services, including BigQuery, Dataflow, Dataproc, Pub/Sub, Cloud Storage, Cloud Composer, Cloud Functions, and Cloud Run.

- Strong proficiency in SQL and Python, with working knowledge of Java or Scala.

- Proven experience delivering at least two large-scale GCP data platforms or migrations.

- Expertise in data modeling, data warehousing, ETL/ELT pipelines, and distributed data processing.

- Experience with Terraform, Git, and CI/CD tools such as Cloud Build, GitHub Actions, or Jenkins.

- Strong knowledge of data governance, lineage, data quality, IAM, encryption, and security compliance.

- Experience with BigQuery cost optimization, performance tuning, and scalable data architecture.

- Excellent stakeholder management, communication, technical leadership, and client-facing skills.

Preferred Qualifications :

- Experience with dbt, Dataform, Apache Kafka, or Spark Structured Streaming.

- Exposure to AWS or Azure data services and multi-cloud environments.

- Knowledge of GenAI/LLM data pipelines using Vertex AI.

- Experience in pre-sales, solution proposals, effort estimation, and technical presentations.