Senior Data Engineer

We are looking for a Senior Data Engineer to design, develop, and optimize our data infrastructure on Databricks
. You will architect scalable pipelines using 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 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/Databricks, 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.

• 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.

Experience

• 3+ years of professional experience in data engineering, with at least 1 year in mobile data

Technical Expertise with GCP Stack

• 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.

Programming & Containerization

• 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.

Data Quality & Governance

• 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 (Online Transaction Processing) and OLAP (Online Analytical Processing) systems.

Communication

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Excellent communication skills for both technical and non-technical audiences.
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