As a Senior DataOps Engineer, you will work closely with Data Engineering, Master Data Management, Data Governance and other Data & AI teams to ensure reliability and availability of Data & Analytics platforms, pipelines and business processes while developing tools to monitor and maintain the scalability and performance of cloud-based environments and operational processes. Your expertise in Site Reliability Engineering (SRE), automation, cloud technologies, and continuous integration of AI capabilities will help streamline our operations and enable faster, more reliable delivery of data-driven solutions. In a modern data ecosystem, it is important to have hands-on experience with Snowflake, Databricks, Azure Cloud Services, DevOps, CI/CD, Python, Shell scripting as well as strong fundamentals in different Platforms such as Enterprise Data Lake/Warehouse (EDL/EDW), and Azure Data Factory. Essential Responsibilities include designing, implementing, and maintaining cloud-based data pipelines and infrastructure solutions for Data & Analytics platforms; streamlining development and operational processes via continuous integration, deployment, automated testing while leveraging Site Reliability Engineering (SRE) principles; utilizing Artificial Intelligence (AI) and Machine Learning (ML) tools to integrate intelligent automation into day-to-day data operations; collaborating with cross-functional teams to identify opportunities leveraging AI/ML to continuously improve business processes and operational decision-making; collaborating with partner teams to define and maintain procedures to ensure data quality, availability, and reliability; automating manual tasks and building processes for SLA/SLI/SLO and error budget tracking; troubleshooting and resolving issues related to cloud infrastructure, application performance, and data pipelines; identifying, designing, and implementing internal process improvements; staying current with DataOps industry best practices and adopting emerging technologies and solutions; driving self service capabilities and ongoing automation initiatives; performing root cause analysis for production issues and recommending improvements; driving efficient resolution for system outages as well as performance and functional shortcomings; mentoring junior DevOps engineers and promoting best practices in cloud technologies, automation, and data operations. The role requires 5+ years of experience as a DataOps Engineer, 3+ years with reporting and visualization tools such as Tableau or Power BI, 3+ years in Enterprise Data Lake/Warehouse (EDL/EDW), and expertise in Azure services, 5+ years leading the end-to-end design and development of scalable services, 5+ years developing, executing and automating operational processes, advanced proficiency with Python, SQL, GitHub, Azure DevOps, Bicep templates, and PowerShell, 5+ years of DevOps experience, experience in cloud cost-savings plans, reviews, and reserved instances, experience in DevOps and SRE practices, testing frameworks, Infrastructure as Code, and building CI/CD pipelines, excellent communication skills, experience in a fast-paced, highly collaborative agile team, effective problem-solving, analytical thinking, and a cloud-native and DevOps mindset, strong documentation, communication, and presentation skills, ability to positively influence team norms, culture, and technical vision, and a Bachelor's/Master’s in computer science or equivalent area. Preferred qualifications include experience with cloud services such as Snowflake, Databricks, Azure Data Factory, Event Hub, Functions, Batch, Key Vault, and Log Analytics, strong experience with popular database programming languages such as SQL, PL/SQL, Stored Procedures, Snowflake SnowPro, Azure, Databricks certifications, familiarity with microservices software architecture and Machine Learning experience, working knowledge of ETL/ELT, CDC, Message-Oriented data movement, API design and stream data integration, and moderate experience with Big Data. Work authorization requires applicants to be currently authorized to work in the United States on a full-time basis with no sponsorship considered. The role is based in Dallas, TX Technology Hub or Richmond, VA Technology Innovation Center with a hybrid work arrangement.
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