<div><b>About the Role</b></div><div><br /></div><div>We’re seeking a GCP Data Analyst with deep expertise in BigQuery, strong SQL and Python skills, and a sharp analytical mindset to support both data validation initiatives and ongoing analytics work. This role is ideal for someone who can navigate large datasets, build robust queries, and identify inconsistencies with precision and insight.</div><div><br /></div><div>The analyst will work across a variety of data workflows, from validating metrics during system migrations to supporting day-to-day data analysis and reporting needs. You’ll leverage advanced BigQuery features—such as authorized views, materialized views, UDFs, partitioning strategies, and time series analysis—to ensure data integrity and surface meaningful insights. Comfort working in Python with data frames and relevant packages is also essential, particularly for tasks involving data manipulation, anomaly detection, or prototyping workflows.</div><div><br /></div><div>A solid understanding of data engineering fundamentals and GCP infrastructure is important, as is the ability to read and interpret code in Java or Scala when collaborating with engineering teammates. Familiarity with Airflow (Composer) will help you understand orchestration logic, though this won’t be a core responsibility. Experience with BigQuery ML, anomaly detection frameworks, or Vertex AI is a plus.</div><h3>Key Responsibilities:-</h3><ul><li>Write, optimize, and execute complex SQL queries in BigQuery to validate data accuracy, identify inconsistencies, and support analytics and reporting.</li><li>Analyze large datasets to assess data quality, compare trends across systems, and surface anomalies or unexpected behaviors.</li><li>Utilize advanced BigQuery features such as authorized views, materialized views, UDFs, partitioned tables, and joins to support scalable, high-performance analysis.</li><li>Use Python (including data frames and relevant libraries) for exploratory analysis, data manipulation, and supporting validation workflows.</li><li>Support time series analysis and, where applicable, anomaly detection using SQL or Python-based approaches.</li><li>Assist with load/transform validation to ensure reliability and accuracy in data pipelines.</li><li>Collaborate with engineering teams to understand data pipelines, with basic ability to read and interpret Java or Scala code when needed.</li><li>Perform side-by-side comparisons of data across systems to ensure consistency during and after migrations.</li><li>Maintain basic familiarity with orchestration tools such as Airflow (Composer) to follow pipeline logic and collaborate effectively with engineering.</li><li>Work within the GCP environment, leveraging cloud tools and services to support analysis, troubleshoot issues, and navigate cloud-based workflows.</li><li>Clearly communicate analytical findings and data quality issues to cross-functional stakeholders to support decision-making.</li></ul><h3>Qualifications:-</h3><ul><li>Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field.</li><li>5+ years of experience in data analyst or analytics engineering roles with strong BigQuery, SQL, and Python skills.</li><li>5+ years of experience building and operating solutions on Google Cloud Platform (GCP).</li><li>Strong ability to write and optimize SQL queries to validate data, analyze trends, and detect inconsistencies.</li><li>Proficient in Python, including use of data frames and common analytical libraries.</li><li>Experience with advanced BigQuery features such as authorized views, materialized views, UDFs, partitions, and time series analysis.</li><li>Strong analytical skills and experience validating data across systems during migrations and ongoing operations.</li><li>Basic ability to read and understand Java or Scala code to support engineering collaboration.</li><li>Familiarity with Airflow (Cloud Composer) to interpret and trace data pipeline workflows.</li></ul><h3>Nice to have:-</h3><ul><li>Familiarity with Looker or other BI tools for metric validation and reporting support.</li><li>BigQuery ML and Vertex AI</li><li>Basic familiarity with legacy systems such as Oozie or Pig for reading existing scripts.</li></ul><h3>Must haves:-</h3><ul><li>Proficiency in SQL, BigQuery, and Python</li><li>Advanced SQL skills in BigQuery for complex data validation, anomaly detection, and trend analysis.</li><li>Experience comparing datasets across systems </li><li>Proven ability to identify and investigate data discrepancies across platforms.</li><li>Strong analytical intuition to sense-check metrics and flag issues that may not trigger formal alerts.</li><li>Ability to perform side-by-side metric and trend comparisons to confirm post-migration accuracy.</li><li>Skilled in root cause analysis using SQL, domain expertise, and supporting context.</li><li>Effective communicator who can document findings and share insights with both technical and non-technical stakeholders.</li><li>Familiarity with time series analysis to detect unexpected shifts, drops, or spikes in metrics.</li><li>Ability to follow structured validation processes while proactively identifying workflow improvements.</li></ul><h3>Travel:-</h3><ul><li>As per business requirements</li></ul><div>The following information is required by pay transparency legislation in the following states: CA, CO, HI, NY, and WA. This information applies only to individuals working in these states. </div><div> </div><div>·       The anticipated starting pay range for Colorado is:  $ 143,700 - $ 210,760</div><div>·       The anticipated starting pay range for the states of Hawaii and New York (not including NYC) is: $ 153,000 - $ 224,400</div><div>·       The anticipated starting pay range for California, New York City and Washington is: $ 167,400 - $ 245,520</div><div> </div><div>Unless already included in the posted pay range and based on eligibility, the role may include variable compensation in the form of bonus, commissions, or other discretionary payments. These discretionary payments are based on company and/or individual performance and may change at any time. Actual compensation is influenced by a wide array of factors including but not limited to skill set, level of experience, licenses and certifications, and specific work location. Information on <a href="https://rackspace.jobs/benefits/">benefits</a> offered is here.</div><div><br /></div><div><span>#LI-VM1 </span></div><div>#LI-Hybrid</div><div><br /></div><div><b>About Rackspace Technology</b></div><div><span>We are the multicloud solutions experts. We combine our expertise with the world’s leading technologies — across applications, data and security — to deliver end-to-end solutions. We have a proven record of advising customers based on their business challenges, designing solutions that scale, building and managing those solutions, and optimizing returns into the future. Named a best place to work, year after year according to Fortune, Forbes and Glassdoor, we attract and develop world-class talent. Join us on our mission to embrace technology, empower customers and deliver the future.</span></div><div><span> </span></div><div><span> </span></div><div><b>More on Rackspace Technology</b></div><div><span>Though we’re all different, Rackers thrive through our connection to a central goal: to be a valued member of a winning team on an inspiring mission. We bring our whole selves to work every day. And we embrace the notion that unique perspectives fuel innovation and enable us to best serve our customers and communities around the globe. We welcome you to apply today and want you to know that we are committed to offering equal employment opportunity without regard to age, color, disability, gender reassignment or identity or expression, genetic information, marital or civil partner status, pregnancy or maternity status, military or veteran status, nationality, ethnic or national origin, race, religion or belief, sexual orientation, or any legally protected characteristic. If you have a disability or special need that requires accommodation, please let us know.</span></div>