Senior Data Engineer

Summary

Join Leadtech as a Senior Data Engineer and design, develop, and optimize our data infrastructure on Google Cloud Platform (GCP). Architect scalable pipelines using various GCP services, ensuring high availability and performance. Build our Data Mart environment, containerize services with Docker and Kubernetes, and implement CI/CD best practices. Leverage Great Expectations for data quality. The ideal candidate possesses extensive knowledge of cloud-native data solutions, strong proficiency with ETL/ELT frameworks, and a passion for building robust, cost-effective pipelines. You will collaborate with Data Science, Analytics, and Product teams. This role offers a flexible schedule, remote work options, and comprehensive benefits.

Requirements

• 3+ years of professional experience in data engineering, with at least 1 year in mobile data
• Proven track record building and maintaining BigQuery environments and Google Cloud Storagebased data lakes
• Deep knowledge of Apache Airflow for scheduling/orchestration and ETL/ELT design
• Experience implementing dbt for data transformations, RabbitMQ 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 DevOps tools (e.g., Terraform, Ansible, Jenkins, GitLab 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 (Online Transaction Processing) and OLAP (Online Analytical Processing) systems
• Excellent communication skills for both technical and non-technical audiences
• High level of organization, self-motivation, and problem-solving aptitude

Responsibilities

• Define and implement the overall data architecture on GCP, including data warehousing in BigQuery, 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 BigQuery, 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

Preferred Qualifications

• Machine Learning (ML) Integration: Familiarity with end-to-end ML workflows and model deployment on GCP (e.g., Vertex AI)
• Advanced Observability: Experience with Prometheus, Grafana, Datadog, or New Relic for system health and performance monitoring
• Security & Compliance: Advanced knowledge of compliance frameworks such as HIPAA, SOC 2, or relevant regulations
• Real-Time Data Architectures: Additional proficiency in Kafka, Spark Streaming, or other streaming solutions
• Certifications: GCP-specific certifications (e.g., Google Professional Data Engineer) are highly desirable

Benefits

• Growth and career development
• At Leadtech, we prioritize your growth. Enjoy a flexible career path with personalized internal training and an annual budget for external learning opportunities
• Work-Life balance
• Benefit from a flexible schedule with flextime (7 - 9:30 a.m. start, 3:30 - 6 p.m. end) and the option of working full remote or from our Barcelona office
• Enjoy free Friday afternoons with a 7-hour workday, plus a 35-hour workweek in July and August so you can savor summer!
• Comprehensive benefits
• Competitive salary, full-time permanent contract, and top-tier private health insurance (including dental and psychological services)
• 25 days of vacation plus your birthday off, with flexible vacation options—no blackout days!
• Unique Perks
• If you wish to come, in our office in Barcelona you’ll find it coplete with free coffee, fresh fruit, snacks, a game room, and a rooftop terrace with stunning Mediterranean views
• Additional benefits include ticket restaurant and nursery vouchers, paid directly from your gross salary