Commify is a leading provider of business messaging solutions, focused on empowering businesses to connect with their customers in meaningful and effective ways. With an extensive global presence, serving over 60,000 businesses across various industries, we are at the forefront of innovation in communications technology. As we continue to expand our offerings, we are looking for talented professionals who share our passion for technology and excellence. Requirements We are seeking a highly experienced Principal Data Engineer to lead our data engineering initiatives. In this role, you will be responsible for designing and implementing robust data architectures and pipelines that enhance our ability to derive meaningful insights from our data. You will play a critical role in driving data-driven decision making and will collaborate closely with cross-functional teams to ensure that data is accessible, reliable, and valuable. Lead the design, development, and implementation of high-performance, scalable, and reliable data pipelines and ETL/ELT processes using Azure Data Factory, Databricks, and other Azure data services. Architect and manage data solutions within the Azure ecosystem, including Azure Data Lake Storage, Databricks, Databricks DLT and Streaming and Event Based Architectures. Drive the adoption of best practices for data governance, data quality, data security, and data lineage. Collaborate closely with data scientists, analysts, and other engineering teams to understand data requirements and translate them into technical solutions. Optimise data processing performance and cost efficiency on Azure Databricks, leveraging Spark capabilities effectively. Develop and maintain robust monitoring, alerting, and logging for data pipelines. Mentor and provide technical guidance to junior and mid-level data engineers, fostering a culture of continuous learning and improvement. Evaluate and recommend new data technologies and tools to enhance our data platform capabilities. Contribute to the overall data strategy and roadmap, ensuring alignment with business objectives. Troubleshoot and resolve complex data-related issues in a timely manner. The Person: Extensive experience as a Data Engineer, with a significant portion in a principal or lead capacity. Deep expertise in Azure data platform services, including: Azure Databricks (extensive hands-on experience with Spark, Python/Scala for real time data processing). Azure Data Factory (maintaining complex data pipelines). Azure Data Lake Storage. Azure SQL Database and/or Azure Synapse Analytics. Strong proficiency in SQL. Exposure to Infrastructure as Code and CICD deployments. Excellent programming skills in Python (Scala is a strong advantage). Proven experience with data modelling, schema design, and data warehousing concepts. Solid understanding of data governance, data quality, and data security principles. Experience with version control systems (e.g., Git). Strong problem-solving abilities and a methodical approach to complex technical challenges. Excellent communication and interpersonal skills, with the ability to articulate complex technical concepts to both technical and non-technical stakeholders. Proven ability to lead and mentor other engineers. Desirable: Experience with real-time data streaming technologies (e.g., Azure Event Hubs, Kafka). Knowledge of CI/CD pipelines for data solutions. Familiarity with containerisation technologies (e.g., Docker, Kubernetes). Experience with other cloud platforms (AWS, GCP) is a plus. Relevant Microsoft Azure certifications (e.g., Azure Data Engineer Associate) Benefits Competitive Salary (£65 - 80,000) Company Bonus scheme Comprehensive healthcare cash plan A generous 27 days of annual leave in addition to Bank Holidays 2 Wellbeing leave days and 2 days dedicated to giving back to your community Enjoy your birthday off! Employer pension contribution at 5% Death in service benefit (4 times your salary) Annual award recognition Fun monthly and quarterly social events Opportunities for training and professional development Flexible hybrid working arrangements