Staff Data Engineer-GCP/Data Solutions

Accountabilities

• Design and implement data patterns that support analytics datasets, including data cleansing, standardization, calculations, validation, and the mapping and linking of information across multiple sources.
• Build, maintain, streamline, and orchestrate complex end-to-end data pipelines across large-scale cloud environments, including GCP and AWS.
• Design and develop scalable data warehousing, data integration, and streaming solutions using technologies such as BigQuery and Databricks.
• Develop large-scale batch and real-time data pipelines using big data Change Data Capture (CDC) processing frameworks and structured streaming technologies.
• Design and implement real-time streaming solutions using Pub/Sub or comparable streaming tables and data lake technologies.
• Build and support complex data pipelines and Application Programming Interfaces (APIs), identifying and integrating complex upstream data sources to enable new capabilities.
• Develop and maintain data integrations supporting application engineering, system integration, analytics, and broader business requirements.
• Perform data validation, quality assurance, automated testing, and end-to-end technical testing to ensure data integrity, system functionality, and solution reliability.
• Design and implement data quality checks and monitoring capabilities aligned with business Service Level Agreements (SLAs) and data quality requirements.
• Diagnose and resolve complex production support issues while maintaining the performance, scalability, reliability, security, and efficiency of data platforms.
• Participate in technical and architectural design reviews with Staff Engineers and adapt engineering patterns based on emerging technologies, tools, and best practices.
• Contribute to the full Software Development Life Cycle (SDLC), including planning, design, development, certification, implementation, testing, and ongoing support.
• Collaborate with Product, Data Science, Application, Analytics, Business, and other technical teams to understand data and infrastructure requirements and resolve complex data-related challenges.
• Work with external technical teams, shared services organizations, and vendors when necessary to deliver timely and high-quality solutions.
• Develop documentation, training materials, technology standards, and engineering guidance for assigned teams.
• Contribute to the refinement of engineering tools, standards, processes, and training across the organization.
• Build complex data models that enable insightful analytics while maintaining high standards of data integrity and quality.
• Apply machine learning concepts and contribute to predictive model implementations, including prompt engineering considerations for AI models.
• Contribute to overall data architecture, modeling, security, scalability, reliability, and performance decisions.
• Mentor and provide technical guidance to Senior Data Engineers while participating in peer reviews and fostering continuous learning across the team.

Requirements

• Bring at least 8 years of professional experience in data engineering or a closely related field.
• Hold a Bachelor’s degree in a relevant discipline or possess equivalent professional experience.
• Have experience working with large-scale cloud infrastructure, large datasets, and mission-critical Service Level Agreements (SLAs).
• Demonstrate advanced proficiency in SQL and Python for data engineering and analytical workloads.
• Hold a data engineering certification from a recognized cloud technology provider.
• Demonstrate experience with cloud data platforms and technologies, particularly GCP and/or AWS, with hands-on experience supporting complex data solutions.
• Have experience with BigQuery, Databricks, data warehousing, data lakes, data meshes, and modern data architecture patterns.
• Demonstrate strong knowledge of data architecture and data modeling best practices and the ability to determine appropriate approaches for different data and analytics platforms.
• Have experience designing and supporting batch and real-time streaming pipelines, including structured streaming, Pub/Sub or comparable technologies, and Change Data Capture (CDC) frameworks.
• Demonstrate strong understanding of coding standards, software design principles, design patterns, testing practices, and secure engineering.
• Have experience with Agile or Lean Startup development methodologies and the ability to operate effectively within iterative delivery environments.
• Possess a strong understanding of business intelligence, analytics, reporting, and application integration.
• Demonstrate strong prioritization and problem-solving skills, with the ability to deliver complex projects despite ambiguity or incomplete information.
• Communicate effectively through strong verbal, written, and data presentation skills, with the ability to work with both technical and business stakeholders.
• Demonstrate the ability to collaborate across multiple teams and locations while contributing positively to shared development environments.
• Be adaptable and willing to learn and apply emerging technologies as data engineering practices evolve.
• Remain calm and effective under pressure and be comfortable working a flexible schedule when business or operational needs require it.
• Demonstrate a collaborative mindset, openness to peer feedback, and willingness to both mentor others and learn from colleagues and technical mentors.
• Be comfortable working in a fast-paced environment with the flexibility to work from home and/or an office as required.

Benefits

• Opportunity to work in a Staff-level data engineering role with broad technical ownership and influence.
• Exposure to large-scale cloud infrastructure, modern data architectures, real-time streaming, analytics, machine learning, and AI-related technologies.
• Collaborative environment involving Product, Data Science, Application, Analytics, Engineering, and Business teams.
• Opportunities to mentor other engineers and contribute to technical standards, architecture, engineering practices, and professional development.
• Flexible work environment with the ability to work from home and/or an office as business needs require.
• Opportunity to work on complex, high-impact data solutions supporting mission-critical business operations.