Data Engineer

We are looking for a Senior
Data Engineer to design, develop, and optimize our data infrastructure on Google Cloud Platform (GCP). You will architect scalable pipelines using Databricks, Big Query, Google Cloud Storage, Apache Airflow, dbt, Dataflow, and Pub/Sub, ensuring high availability and performance across our ETL/ELT processes. You will leverage Great Expectations to enforce data quality standards. The role also involves building our Data Mart (Data Mach) environment, containerizing services with Docker and Kubernetes (K8s), and implementing CI/CD best practices.

A successful candidate has extensive knowledge of cloud-native data solutions, strong proficiency with ETL/ELT frameworks (including dbt), and a passion for building robust, cost-effective pipelines.

• Define and implement the overall data architecture on GCP, including data warehousing in Big Query, data lake patterns in Google Cloud Storage, and Data Mart (Data Mach) solutions
• Integrate Terraform for Infrastructure as Code to provision and manage cloud resources efficiently
• Establish both batch and real-time data processing frameworks to ensure reliability, scalability, and cost efficiency

• Design, build, and optimize ETL/ELT pipelines using Apache Airflow for workflow orchestration
• Implement dbt (Data Build Tool) transformations to maintain version-controlled data models in Big Query, ensuring consistency and reliability across the data pipeline
• Use Google Dataflow (based on Apache Beam) and Pub/Sub for large-scale streaming/batch data processing and ingestion
• Automate job scheduling and data transformations to deliver timely insights for analytics, machine learning, and reporting

• Implement event-driven or asynchronous data workflows between microservices
• Employ Docker and Kubernetes (K8s) for containerization and orchestration, enabling flexible and efficient microservices-based data workflows
• Implement CI/CD pipelines for streamlined development, testing, and deployment of data engineering components

• Enforce data quality standards using Great Expectations or similar frameworks, defining and validating expectations for critical datasets
• Define and uphold metadata management, data lineage, and auditing standards to ensure trustworthy datasets
• Implement security best practices, including encryption at rest and in transit, Identity and Access Management (IAM), and compliance with GDPR or CCPA where applicable

• Integrate with Looker (or similar BI tools) to provide data consumers with intuitive dashboards and real-time insights
• Collaborate with Data Science, Analytics, and Product teams to ensure the data infrastructure supports advanced analytics, including machine learning initiatives
• Maintain Data Mart (Data Mach) environments that cater to specific business domains, optimizing access and performance for key stakeholders

• 3+ years of professional experience in data engineering, with at least 1 year in mobile data
• Proven track record building and maintaining Big Query environments and Google Cloud Storage based data lakes
• Deep knowledge of Apache Airflow for scheduling/orchestration and ETL/ELT design
• Experience implementing dbt for data transformations, Rabbit
  
  MQ for event-driven workflows, and Pub/Sub + Dataflow for streaming/batch data pipelines
• Familiarity with designing and implementing Data Mart (Data Mach) solutions, as well as using Terraform for IaC
• Strong coding capabilities in Python, Java, or Scala, plus scripting for automation
• Experience with Docker and Kubernetes (K8s) for containerizing data-related services
• Hands‑on with CI/CD pipelines and Dev Ops tools (e.g., Terraform, Ansible, Jenkins, Git Lab CI) to manage infrastructure and deployments
• Proficiency in Great Expectations (or similar) to define and enforce data quality standards
• Expertise in designing systems for data lineage, metadata management, and compliance (GDPR, CCPA)
• Strong understanding of OLTP and OLAP systems
• Excellent communication skills for both technical and non‑technical…