Senior Data Engineer

We need to output HTML fragment with sections: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections that have real specific content. Must not repeat job title. Must not include Job ID etc. Must remove boilerplate like EEO, legal, accommodation, etc. Must preserve all substantive info. We need to parse the posting. Let's extract sections: - Intro: "We are hiring a Senior Data Engineer who will help transform enterprise data into reliable, scalable solutions by developing modern data pipelines and partnering closely with analytics, data science, and engineering teams." Then there is a block: "Helping Our People Thrive Offering competitive wages and benefits, that support your life both in and out of workProviding a flexible hybrid work schedule, meaning we expect the office to be your primary place of work, balanced with choice and control.Creating continuous learning opportunities to help you grow and upskill.Fostering a culture of inclusion where employees feel seen, heard and valued — and living it out every day.Empowering you to make a meaningful impact on people and the planet through your work and Steelcase’s ongoing commitment." This seems like benefits and schedule info. We need to separate. Then "Supporting Meaningful Work Collaborate with product owners, managers and engineers, help with scoping and defining Minimum Viable Products (MVPs).Collaborate with multi-functional teams to define, design, and build big data solutions using tools and programming languages like Databricks, Azure Data Factory, Apache Spark, Python, SQL, etc.Architect and develop scalable and robust data pipelines using data from diverse sources, databases, APIs, applications and files (e.g. Snowflake, Azure Synapse, AWS Redshift, GCP Big Query).Apply data architecture patterns (e.g., event-driven, medallion, data lakehouse) to ensure scalability, performance, and maintainability of data solutions.Perform data mapping, establish data lineage, define data contracts, and document information flows to ensure observability and traceability (e.g. Azure Purview, Lakehouse Monitoring).Collaborate with data consumers (e.g. data analytics stakeholders and data scientists) to streamline the data acquisition and curation process.Monitor, optimize and troubleshoot data pipeline performance issues and coordinate the issue resolution process with the respective individuals/partner teams.Research and promote new tools and techniques to shape the future of the data platform, and build POCs to validate these new concepts including, but not limited to, data processing frameworks, distributed storage systems, data orchestration and workflow tools. (e.g. Databricks Lakebase, Azure Event Hubs, Azure Stream Analytics).Implement an enterprise data governance model (e.g., Databricks Unity Catalog) and actively promote data protection, sharing, reuse, quality, and standards.Architect, develop, and manage the data platform infrastructure, ensuring high availability, scalability, and security, utilizing multiple methods such as infrastructure-as-code (IaC) and CI/CD pipelines (e.g. Azure DevOps)." That's responsibilities. Then "Minimum QualificationsAt least 3 years of full-time experience in US as Data Engineer, Data Scientist, AI Engineer, Software engineer or similar position.Hands-on experience with Databricks, Apache Spark, Azure, and Python.Strong understanding of data engineering concepts, including data pipelines and scalable data processing solutions.Qualified applicants must be authorized to work in the United States on a full-time basis. Steelcase will not provide support for or sponsor work authorization and/or visas for this role." That's qualifications/requirements. Then "Desired Skills And ExperienceExperience with Data Governance practices and frameworks.Experience working with cloud platforms. While Azure experience is preferred, candidates with relevant experience in AWS or Google Cloud Platform (GCP) are also encouraged to apply.Knowledge of Scala is preferred." That's desired skills. Then "Doing better for people and planet Steelcase is a global design and thought leader in the world of work. Along with our expansive community of brands, we design and manufacture innovative furnishings and solutions to help people do their best work in the many places where work happens. Why People Choose to Work with Us: At Steelcase, we put people at the center of everything we do. We understand the role of work and believe that it can bring meaning and purpose to the lives of our customers and our employees. We prioritize supporting our employees both in and out of work, in all aspects of their lives. When we bring our talents together, we make a positive lasting impact through our work and communities." That's about the company, maybe "About the role"? Actually "About the role" is earlier: "We are hiring a Senior Data Engineer who will help transform enterprise data into reliable, scalable solutions by developing modern data pipelines and partnering closely with analytics, data science, and engineering teams." That's a short description. Then "The starting annual base salary range for this position is $120,000 – $140,000. Please note that the salary information is a general guideline only. Steelcase considers factors such as (but not limited to) scope and responsibilities of the position, candidate's work experience, education/training, key skills, internal peer equity, as well as market and business considerations when extending an offer." That's Pay. Then EEO statement etc we must remove. Also "If we can make the application process easier through accommodation, please email us at myhr@steelcase.com." That's application instruction, remove. Now we need to map to sections: - About the role: maybe the intro paragraph. Could also include "Helping Our People Thrive" etc? But that is benefits and schedule. We'll treat "Helping Our People Thrive" as Benefits and Schedule. But we need to separate Benefits, Pay, Schedule. From "Helping Our People Thrive": "Offering competitive wages and benefits, that support your life both in and out of workProviding a flexible hybrid work schedule, meaning we expect the office to be your primary place of work, balanced with choice and control.Creating continuous learning opportunities to help you grow and upskill.Fostering a culture of inclusion where employees feel seen, heard and valued — and living it out every day.Empowering you to make a meaningful impact on people and the planet through your work and Steelcase’s ongoing commitment." We can extract: - Benefits: competitive wages and benefits supporting life in/out of work, continuous learning opportunities, culture of inclusion, meaningful impact on people and planet. - Schedule: flexible hybrid work schedule, office primary place of work, balanced with choice and control. We need to avoid filler like "competitive wages and benefits" is okay. "continuous learning opportunities" is benefit. "culture of inclusion" benefit. "meaningful impact" maybe benefit. We'll create Benefits

with items: Competitive wages and benefits supporting life in and out of work; Continuous learning opportunities to grow and upskill; Culture of inclusion where employees feel seen, heard and valued; Opportunity to make a meaningful impact on people and the planet through work and Steelcase’s commitment. Schedule: Flexible hybrid work schedule; Office expected as primary place of work, balanced with choice and control. Now Pay: Starting annual base salary range $120,000 – $140,000; note that salary is guideline etc. We'll include that as prose. Now About the role: The initial sentence: "We are hiring a Senior Data Engineer who will help transform enterprise data into reliable, scalable solutions by developing modern data pipelines and partnering closely with analytics, data science, and engineering teams." Could also include "Helping Our People Thrive"? But we already used that. So About the role is that sentence. Responsibilities: The big block after "Supporting Meaningful Work". We'll list as bullet points. Requirements: Might be same as Minimum Qualifications? The instructions: Use sections: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. They differentiate Requirements and Qualifications and Skills. We need to decide what goes where. We have Minimum Qualifications (experience, hands-on with Databricks, Spark, Azure, Python, understanding of data engineering concepts, work authorization). That's qualifications. Desired Skills And Experience: Data Governance practices, cloud platforms (Azure preferred, AWS/GCP also), Scala knowledge preferred. That's skills. Requirements could be maybe the same as Minimum Qualifications? But we need both Requirements and Qualifications sections. Perhaps Requirements = mandatory requirements (like experience, authorization). Qualifications = preferred qualifications? But we already have Desired Skills And Experience as skills. Hmm. We could treat: - Requirements: Minimum Qualifications (mandatory). - Qualifications: maybe also include "Qualified applicants must be authorized to work in the United States on a full-time basis." but that's already in Minimum Qualifications. Could split: Requirements = experience and hands-on; Qualifications = understanding of data engineering concepts and work authorization? But that seems arbitrary. Better: Use Requirements for the mandatory experience and technical skills (3 years experience, hands-on with Databricks, Spark, Azure, Python). Use Qualifications for the understanding of data engineering concepts and work authorization (maybe also "Qualified applicants must be authorized..."). Skills: Desired Skills And Experience (Data Governance, cloud platforms, Scala). But we need to ensure we don't duplicate. Let's extract: Minimum Qualifications sentences: 1. "At least 3 years of full-time experience in US as Data Engineer, Data Scientist, AI Engineer, Software engineer or similar position." 2. "Hands-on experience with Databricks, Apache Spark, Azure, and Python." 3. "Strong understanding of data engineering concepts, including data pipelines and scalable data processing solutions." 4. "Qualified applicants must be authorized to work in the United States on a full-time basis. Steelcase will not provide support for or sponsor work authorization and/or visas for this role." We could split: - Requirements: items 1 and 2 (experience and hands-on). - Qualifications: items 3 and 4 (understanding concepts and work authorization). Skills: Desired Skills And Experience. Now we need to ensure we don't include boilerplate like "Steelcase will not provide support for or sponsor work authorization and/or visas for this role." That's substantive, keep. Now Benefits and Schedule we already have. Pay: we have. Now About the role: we have. Now Responsibilities: we need to list bullet points from the big block. Let's parse responsibilities block into distinct duties: - Collaborate with product owners, managers and engineers, help with scoping and defining Minimum Viable Products (MVPs). - Collaborate with multi-functional teams to define, design, and build big data solutions using tools and programming languages like Databricks, Azure Data Factory, Apache Spark, Python, SQL, etc. - Architect and develop scalable and robust data pipelines using data from diverse sources, databases, APIs, applications and files (e.g. Snowflake, Azure Synapse, AWS Redshift, GCP Big Query). - Apply data architecture patterns (e.g., event-driven, medallion, data lakehouse) to ensure scalability, performance, and maintainability of data solutions. - Perform data mapping, establish data lineage, define data contracts, and document information flows to ensure observability and traceability (e.g. Azure Purview, Lakehouse Monitoring). - Collaborate with data consumers (e.g. data analytics stakeholders and data scientists) to streamline the data acquisition and curation process. - Monitor, optimize and troubleshoot data pipeline performance issues and coordinate the issue resolution process with the respective individuals/partner teams. - Research and promote new tools and techniques to shape the future of the data platform, and build POCs to validate these new concepts including, but not limited to, data processing frameworks, distributed storage systems, data orchestration and workflow tools. (e.g. Databricks Lakebase, Azure Event Hubs, Azure Stream Analytics). - Implement an enterprise data governance model (e.g., Databricks Unity Catalog) and actively promote data protection, sharing, reuse, quality, and standards. - Architect, develop, and manage the data platform infrastructure, ensuring high availability, scalability, and security, utilizing multiple methods such as infrastructure-as-code (IaC) and CI/CD pipelines (e.g. Azure DevOps). We'll put each as