Senior Data Architect

Onix Networking Corp. - Lakewood, OH

Full-time - Senior

Remote - Lakewood, OH

Professional, Scientific, and Technical Services

About the position

Onix is a trusted cloud consulting company that specializes in providing cloud-powered solutions and data migration products to help businesses maximize their technology investments. With over 20 years of experience in the cloud sector, Onix has developed a reputation for delivering exceptional results through a combination of technology expertise and innovative data and AI solutions. The company offers a wide range of services tailored to meet the unique needs of its clients, including advanced cloud security solutions and innovative AI capabilities. We are currently seeking a Senior Data Architect/Principal Data Engineer who will be instrumental in designing and implementing data solutions that align with client business needs and support the overall cloud data architecture. The ideal candidate will possess a strong understanding of Lakehouse Architecture, data modeling, and data management, with a significant focus on data warehouse and Lakehouse design and development. Experience with cloud platforms such as Azure, AWS, and GCP is essential for this role. In this position, you will lead the design, development, and implementation of Lakehouse and data warehouse architectures in a cloud-based environment. You will collaborate closely with business stakeholders to interpret their data needs and translate them into technical requirements. A solid understanding of data governance is crucial, as you will be responsible for developing and implementing data governance policies and procedures to ensure data quality and integrity. You will also manage cloud data environments in accordance with company security guidelines and lead a team of data engineers to implement your solution designs. Staying current with emerging trends and technologies in the data and AI/ML field will be key to educating delivery teams on new cloud-based data analytics initiatives.

Responsibilities

• Lead the design, development, and implementation of Lakehouse and data warehouse architecture in a cloud-based data environment.
• Work closely with business stakeholders to interpret their data needs and translate them into technical requirements.
• Develop and implement data governance policies and procedures to ensure data quality and integrity.
• Implement data testing and validation processes to maintain data quality.
• Deploy and debug cloud data initiatives in accordance with best practices throughout the development lifecycle.
• Manage cloud data environments in accordance with company security guidelines.
• Design and build reusable, repeatable solutions and components for future use.
• Mentor a team of data engineers to implement solution designs.
• Stay abreast of current and emerging trends and technologies in the data and AI/ML field.
• Partner with delivery and support teams to find opportunities to reduce manual effort in deployments.
• Consult on professional services engagements to help customers design and implement data warehouse solutions.
• Provide client presentations to review project design, outcomes, and recommendations.
• Employ exceptional problem-solving skills to address issues proactively.
• Lead the orchestration and automation of cloud-based data platforms.

Requirements

• 7+ years of experience in Data Architecture, data engineering, and analytics, including performance tuning, pipeline integration, and infrastructure configuration.
• 10+ years of consulting experience.
• Completed Databricks Data Engineering Professional/Associate certification or Cloud Certification (Azure, AWS, GCP).
• Working knowledge of two or more common Cloud ecosystems (AWS, Azure, GCP) with deep expertise in at least one.
• Deep experience with distributed computing using Spark, including knowledge of Spark runtime internals and Spark Structured Streaming.
• Working knowledge of MLOps with a strong understanding of essential components of MLOps Architecture.
• Current knowledge of Databricks product and platform features.
• Familiarity with optimizations for performance and scalability.
• Data pipelining experience leveraging Databricks Delta Live Tables and Data Built Tool (DBT).
• Experience with Terraform, Git, CI/CD tools, as well as automation and integration testing.
• Thorough understanding of Databricks Delta, Iceberg, and Hudi.
• Experience in Spark best practices, including notebook and cluster creation and configuration.
• Understanding of ingestion patterns and data quality enforcement techniques within Databricks, Spark, and DLT.
• Understanding of constraints, expectations, CDC, CDF, SCD Type 1/Type 2.
• Understanding of Unity Catalog and DBX governance/security models.
• Familiarity with leveraging Databricks REST APIs for testing and deployment.