Senior Data Architect opening at SADA company

• Required Travel - 10% travel to customer sites, conferences, and other related events. Customer Facing - You will interact with customers on a regular basis, sometimes daily, other times weekly/bi-weekly.

• Common touch points occur when qualifying potential opportunities, at project kickoff, throughout the engagement as progress is communicated, and at project close. You can expect to interact with a range of customer stakeholders, including engineers, technical project managers, and executives. You will serve as a Tier 3 on-call escalation point for our largest customers’ business critical workloads.
• On boarding/Training - The first several weeks of on boarding are dedicated to learning and will encompass learning materials/assignments and compliance training, as well as meetings with relevant individuals. Details of the timeline are shared closer to the start date.

Experience: Minimum of 5 years of experience in data architecture, data engineering, or a similar role., * Bachelor’s degree in Computer Science, Information Systems, or a related field.

• Proven experience in designing and implementing data solutions using Google Cloud Platform (GCP) services such as BigQuery, Cloud Composer, Cloud SQL, Dataproc, and Dataflow.
• Expertise in designing, building, and maintaining ETL pipelines using various data integration tools and frameworks.
• Strong knowledge of ETL processes including data cleansing, data conversion, data import/export, data validation and testing.
• Expertise in extending and customizing monitoring solutions for resource metrics, input/output metrics, and transformation stage metrics.
• Proficient in BigQuery table archival and restore processes to optimize cost and on-demand data warehousing capabilities.
• Hands-on experience in building data quality monitors and extending data quality reporting across various stages of data pipelines.
• Experience in pipeline execution visualization and orchestration using Cloud Composer or alternative lightweight microservices.
• Strong knowledge of creating reusable data transformation templates and libraries for efficient data pipeline deployment.
• Familiarity with handling data mutations and creating streaming ingestion templates for specific customer needs.
• Proficiency in log analytics and observability using Looker and BigQuery, with a focus on leveraging cloud logging data.
• Experience conducting feasibility studies and working with API-based data warehouse exposure, preferably using GraphQL.
• Solid understanding of MLOps principles, including model deployment, monitoring, versioning, and continuous integration/continuous deployment (CI/CD) of machine learning pipelines.
• Excellent problem-solving skills, attention to detail, and ability to work both independently and collaboratively within a team.
• Strong communication skills, with the ability to effectively convey technical concepts to both technical and non-technical stakeholders. Strong estimation skills