GCP Data Engineer

Summary
We are seeking a Senior Data Engineer with a distinguished background in Google Cloud Platform (GCP) to spearhead the evolution of our enterprise data ecosystem. With 5-8 years of professional experience, the successful candidate will operate as a technical authority, designing and deploying sophisticated data architectures that bridge the gap between complex raw data and strategic business intelligence. This role demands a mastery of distributed computing, advanced Python development, and expert-level SQL optimization to ensure the integrity, scalability, and cost-efficiency of our global data assets.

Core Responsibilities

1. Architectural Strategy & System Design
Enterprise Framework Design: Conceptualize and implement end-to-end data architectures utilizing GCP s Modern Data Stack (BigQuery, Dataflow, Dataform).

Scalable Data Modeling: Lead the development of high-performance data models (Star, Snowflake, Data Vault) optimized for multi-petabyte scale and high-concurrency analytics.

Hybrid & Multi-Cloud Strategy: Provide technical leadership on data integration strategies spanning GCP, on-premise systems, and third-party SaaS environments.

2. Advanced Engineering & Pipeline Automation
Distributed Processing: Engineer highly resilient, low-latency streaming and batch pipelines using Apache Beam (Dataflow) and Cloud Composer (Airflow).

Software Engineering Excellence: Develop reusable Python libraries and frameworks to standardize data ingestion, logging, and error-handling across the engineering team.

Infrastructure as Code (IaC): Drive operational maturity by managing cloud resources exclusively through Terraform, ensuring robust versioning and environment parity.

3. Data Governance, Security & Performance
System Optimization: Conduct deep-dive performance tuning of BigQuery environments, implementing partitioning, clustering, and slot management to optimize ROI.

Security & Compliance: Architect data security protocols including VPC Service Controls, IAM Least Privilege, and data masking/encryption to meet global compliance standards (GDPR, SOC2).

Observability: Establish comprehensive monitoring and alerting frameworks for data health, ensuring high availability and meeting stringent Service Level Objectives (SLOs).

4. Technical Leadership & Collaboration
Strategic Mentorship: Serve as a mentor to mid-level and junior engineers, conducting rigorous code reviews and promoting best practices in DataOps.

Stakeholder Alignment: Act as a primary technical liaison between Data Science, Business Intelligence, and Executive leadership to translate business goals into technical roadmaps.