The Lead/Senior Data Architect will design, implement, and manage scalable data architectures using Google Cloud Platform (GCP), BigQuery, and ETL tools
- Key responsibilities include developing robust data pipelines, leveraging cloud engineering, and managing cloud-based APIs and serverless solutions
- The candidate will lead a team of data engineers, implement best practices, and ensure data platform accuracy, reliability, and scalability
- Strong problem-solving, communication, and collaboration skills are essential
- The role also involves in ensuring data security and compliance, and staying updated with emerging technologies to drive innovation and meet business needs
Google Cloud Platform (GCP):
- Design and manage robust, scalable data architectures using GCPs suite of tools and services.
- Implement efficient data flows and data pipelines to streamline data processing and integration.
- Utilize Big Query (BQ) for advanced data management, analysis, and querying capabilities, ensuring optimal performance and scalability.
Analytical Warehousing:
- Hands-on experience with designing, implementing, and managing analytical warehousing applications.
- Proficiency in Big Query for efficient data storage, querying, and analysis.
- Design and implement data security and data privacy measures to ensure compliance with industry and regulatory standards.
- Familiarity with Fivetran for seamless data integration and Power BI for comprehensive data visualization and reporting
ETL Tools:
- Expertise in ETL tools such as Airflow, Apache Spark, Kafka, and PubSub for efficient data processing.
- Develop and maintain robust ETL processes to ensure accurate, timely, and reliable data integration across various platforms.
- Define and execute the data engineering roadmap to support the companys long-term goals, including data modeling, data ingestion, and data visualization.
Cloud Engineering:
- Develop and manage cloud-based APIs, specifically using Flask for creating scalable and efficient endpoints.
- Execute complex SQL queries for comprehensive data manipulation and analysis.
- Implement serverless computing solutions to optimize resource usage and scalability.
- Experience with Docker for containerization, ensuring consistent and portable application deployment.
Best Practices:
- Develop and implement data engineering best practices to ensure the accuracy, reliability, and scalability of the data platform.
- Take ownership and accountability of the best market practices.
- Work with the team to re-engineer and transform data practices and architecture, enhancing performance to meet business needs.
Data Roadmap:
- Identify and involve key stakeholders from different business units and ensure continuous communication to gather requirements and feedback .
- Analyse the existing data infrastructure and processes identify gaps, inefficiencies, and opportunities for improvement.
- Continuously monitor the performance of data systems and processes and evaluate the impact on business objectives and make necessary adjustments.
Cross-Functional Collaboration:
- Collaborate with cross-functional teams such as analytics, application development, and product to understand business needs and provide data-driven insights.
- Develop and maintain relationships with key stakeholders.
- Communicate complex technical concepts to non-technical stakeholders and senior leadership.
Innovation and Technology:
- Drive innovation by continuously evaluating and recommending new data technologies and tools to improve data engineering capabilities and practices.
- Stay up to date with emerging technologies and industry trends to identify opportunities for innovation and improvement.
