ARCHITECT TECHNOLOGY, DATA

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Position Overview:

The Architect is a responsible for designing scalable, reliable, and innovative solutions that support business objectives and deliver exceptional user experiences. Working closely with product, engineering, and business teams, they define technical direction, establish architectural standards, and guide the design and delivery of technology solutions across the product lifecycle.

Architects provide technical leadership through collaboration, mentorship, and knowledge sharing. They create and maintain architectural frameworks, standards, and documentation, promote best practices, and help ensure solutions are secure, maintainable, and aligned with enterprise strategy. They also influence technology decisions, foster cross-functional partnerships, and drive continuous improvement across the organization.

Delivery & Execution

- Architect, Develop, and Deploy: Design, develop, and deploy secure, reliable, and scalable software solutions that are cost-effective and sustainable, meeting specific business requirements.
- Strategic Planning & Governance: Design and review comprehensive architectural solutions and roadmaps. Ensure these plans align with Enterprise Standards and best practices to facilitate seamless integration across distributed systems.
- Risk Management: Identify, communicate, and proactively mitigate technical and business risks across the solution's application, data, and technology domains.
- Documentation & Communication: Create clear and meaningful architecture diagrams, business process flows, and technical documentation tailored for various stakeholder audiences.
- Success Measurement: Define Key Performance Indicators (KPIs) to rigorously measure the success of the solution and the overall platform, covering categories such as Business Value, Data Quality, Performance, and Governance.
- Technical Leadership: Provide expert technical guidance and mentorship to the development team, leading the resolution of complex technical issues during the implementation phase.
- Quality Assurance Oversight: Oversee unit, integration, and functional/non-functional testing activities to ensure the system consistently meets both business and stringent non-functional requirements, including security, performance, and reliability.
- Deployment Strategy: Develop and lead the rollout strategy, including go-live plans, data migration procedures, and the cutover plan, ensuring the successful implementation of major business and technology initiatives.
- Security Remediation: Identify vulnerabilities in the codebase (with or without automated tooling) and implement timely and effective security fixes.
- Lakehouse Architecture: Define and own the lakehouse architecture, including open table formats, metastore and catalog strategy and storage/compute separation across GCS, BigQuery, and downstream serving engines.
- Semantic Layer: Architect and govern the enterprise semantic layer, including data modeling standards, metric definitions, pre-aggregation design, and access policies, as the single source of business logic for BI tools, APIs, and AI/NL2SSR use cases.
- Analytics Serving Layer: Design and optimize the StarRocks serving layer, including table and partitioning design, materialized views, external catalogs over the lakehouse, query performance tuning, and capacity planning.
- Data Standards & Onboarding: Define medallion (bronze/silver/gold) layering standards, data contracts, and domain onboarding patterns so new domains land on the platform consistently and securely.
- Data Governance Platform: Establish and evolve data cataloging, metadata management, column-level lineage, data quality checks, data classification, and policy-based access control.
- Cloud Cost Governance: Drive workload optimization, query cost monitoring, and right-sizing across services.
- Techno-Functional Leadership: Lead collaboration across data engineering, data science, analytics, product, platform, operations, security, and business teams to translate business requirements into scalable technical designs, and act as the escalation point for techno-functional challenges to unblock delivery quickly.
- Architecture Decisions: Set reference architectures and reusable patterns, and document key decisions as Architecture Decision Records (ADRs) to keep the platform coherent across teams.
- AI in the Data Engineering SDLC: Drive adoption of AI-assisted design and code generation, automated code review, test generation, data profiling, lineage mapping, and documentation to improve speed, quality, and consistency.

Support & Enablement

- Incident Leadership & Triage: Serve as the primary point of escalation for high-priority incidents and outages, efficiently triaging and leading resolution efforts to minimize service disruption.
- Cross-Functional Collaboration & Governance: Lead collaboration with multiple cross-functional teams on solution design. Champion architectural alignment by successfully navigating the Architecture Review Board governance process and ensuring adherence to enterprise standards.
- Operational Excellence & Monitoring: Proactively define and monitor Service Level Objectives (SLOs) for production solutions using observability tools. Conduct continuous performance and capacity reviews across the entire production stack, including code, infrastructure, data pipelines, and message processing.
- Technical Consultation: Act as a subject matter expert by providing direct technical consultation and support to operations team, and other cross-functional groups regarding deployed software.
- Vendor Engagement: Work actively with external vendors to identify, advocate for, and implement valuable feature enhancements and improvements within core software products.

Learning

- AI-Assisted Engineering: Leverage AI-assisted engineering tools to improve productivity and quality in architecture, design, and coding, and share effective practices with the team. Example tools: Claude Code GitHub Copilot, SAP Joule, Joule for Consultant, Joule for Developer
- Pioneer Modern Application, Data & AI Practices: Actively engage in and lead training, workshops, and knowledge-sharing sessions focused on modern software design principles, core development practices, and architectural patterns relevant to scalable application, data and AI solutions.
- Continuous Skill & Technology Acquisition: Systematically research, study, and master emerging technologies, architectural best practices, and innovative AI methodologies through diverse resources (e.g., industry white papers, specialized tutorials, video courses).
- External Knowledge Integration: Represent the organization at relevant industry conferences, and technical forums. Critically evaluate and synthesize knowledge gained to propose the application of new technologies that can drive business value and platform modernization.
- Strategic Technology Foresight: Maintain a proactive, deep awareness of the latest trends, tools, and advancements in the broader Data Analytics, AI, and Machine Learning ecosystems. Identify, evaluate, and champion new tools or methodologies that significantly enhance the efficiency, performance, and future-readiness of the enterprise data platform.

Team Leadership & Mentorship:

- Serve as a technical leader and mentor, fostering a culture of collaboration, architectural excellence, and continuous learning while developing the next generation of technology talent.

Skills:

- Excellent analytical and problem-solving abilities.
- Excellent verbal and written communication skills, including the ability to present complex technical solutions to both technical and non-technical audiences.
- Proven experience leading technical conversations with business partners and vendors.
- Strong knowledge of software development methodologies (e.g., Agile, Scrum).
- Ability to work independently with minimal direction while collaborating effectively across cross-functional teams.
- Proven history of mentoring and developing junior/mid-level engineers, with a willingness to continually learn new technologies and concepts.
- Hands-on experience designing lakehouse architectures using open table formats (Apache Iceberg, Delta Lake, or Hudi) and associated catalogs and metastores, with proven experience in data modeling, data warehousing, and medallion layering best practices.
- Strong command of SQL and experience architecting scalable ETL/ELT pipelines for both structured and unstructured data using transformation tools such as Dataform or dbt, with hands-on Java, JavaScript, and Python development experience, including the ability to identify root cause and remediate code quality issues.
- Experience with orchestration frameworks such as Apache Airflow/Cloud Composer and batch/stream processing frameworks such as Apache Beam (Dataflow), Spark (Dataproc), and Pub/Sub.
- Proficient with SQL and NoSQL databases such as PostgreSQL, Cloud SQL, Redis, and Bigtable, and GCP storage solutions such as Cloud Storage and BigQuery.
- Experience with high-concurrency OLAP engines (StarRocks preferred; ClickHouse, Apache Doris, Druid, or Pinot also relevant) and semantic layer technologies (Cube.dev preferred; dbt Semantic Layer, AtScale, or LookML), including schema design, metric modeling, pre-aggregations, caching, and performance tuning.
- Familiarity with data visualization tools such as Looker and Tableau.
- Proven experience defining and enforcing data quality, data governance, and compliance policies using governance and metadata platforms (e.g., Dataplex, OpenMetadata, Collibra, DataHub) and data quality tooling (e.g., Soda, Great Expectations), including column-level lineage, cataloging, classification, and policy-based access control.
- Experience defining monitoring and alerting for data pipelines using tools such as Grafana and Prometheus to ensure high availability and reliability.
- Proven experience optimizing data storage and processing for performance and cost across cloud and on-prem environments, with an in-depth understanding of cloud design patterns, operations, and cost models, including workload optimization, query cost monitoring, and right-sizing.
- Proven experience migrating on-premises data stacks to the cloud, including SQL Server data platforms and SAP BW, covering current-state assessment, target architecture, data and pipeline migration, reconciliation and validation, and cutover.
- Hands-on experience applying generative AI and LLM-based tools (e.g., Claude, GitHub Copilot, Gemini) in the SDLC, with a track record of measurably improving delivery efficiency and code quality.
- Understanding of AI integration patterns such as agents, MCP/tool connectors, and retrieval-augmented generation, and how to apply them to data platform workflows.
- Experience with SAP technologies (e.g., SAP Datasphere, BW, S/4HANA data extraction) is an asset; retail experience preferred.

Minimum Years of Work Experience:

- 10+ years of related experience, including 5+ years in data and application architecture design and development, with deep expertise designing scalable, secure, and high-performance data platforms for enterprise analytics and operational workloads, and the ability to review pipelines and coach data engineers.

The pay range for this position is between $105,000.00 - $125,250.00