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Key Responsibilities and Required Skills for Analytics Architect

💰 $140,000 - $210,000

AnalyticsData ArchitectureBusiness IntelligenceCloud Data Platforms

🎯 Role Definition

The Analytics Architect is a senior technical and strategic role responsible for designing, implementing, and governing the analytics and data architecture that enables enterprise reporting, self-service BI, advanced analytics, and ML initiatives. This role combines deep hands-on technical expertise (data modeling, cloud data platform engineering, ETL/ELT, BI tool design) with cross-functional leadership—partnering with product, engineering, data science, and business stakeholders to translate analytic requirements into scalable, secure, performant data solutions. The Analytics Architect defines roadmaps for data platform modernization, establishes analytics best practices, enforces governance and metadata standards, and ensures data products are reliable, discoverable, and aligned to business outcomes.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Senior Data Engineer with strong experience in data warehousing and cloud platforms
  • Senior Business Intelligence Engineer / Lead BI Developer with platform architecture exposure
  • Analytics Engineering Lead with dbt, ELT and data modeling experience

Advancement To:

  • Head of Data Architecture / Director of Analytics Architecture
  • Chief Data Officer (CDO) or VP of Data & Analytics
  • Enterprise Architect with a focus on data and analytics

Lateral Moves:

  • Data Platform Engineering Lead
  • Data Science Engineering Manager
  • Product Manager for Analytics Platforms

Core Responsibilities

Primary Functions

  • Architect and own the enterprise analytics data architecture end-to-end, including the design of cloud data warehouses/lakes (e.g., Snowflake, BigQuery, Redshift), ELT/ETL patterns, data marts, semantic layers, and BI consumption models to deliver scalable, secure, and cost-effective analytics.
  • Define and enforce data modeling standards (dimensional modeling, star schemas, slowly changing dimensions, conformed dimensions) and best practices for building reusable, maintainable analytical datasets that support reporting, dashboards, and advanced analytics.
  • Lead design and implementation of ELT/ETL pipelines using modern analytics engineering tools (dbt, Airflow, Prefect, Spark) and ensure CI/CD, automated testing, and version control are applied to data pipelines and transformations.
  • Collaborate with analytics engineering, data engineering, and data science teams to translate product and business requirements into concrete data products, schemas, and API endpoints that power ML models, operational reporting, and self-service analytics.
  • Establish and manage a semantic layer or BI abstraction (LookML, Power BI datasets, Tableau semantic model, or BI tools’ governed layer) to ensure consistent KPIs, metrics definitions, and a single source of truth for business users.
  • Design and implement data governance, cataloging, and metadata management solutions (Collibra, Alation, Amundsen, Data Catalogs) to enable data discoverability, lineage, and compliance across the analytics estate.
  • Drive performance optimization strategies including partitioning, clustering, materialized views, cost-based query optimization, and resource governance to minimize cost and maximize query performance on cloud platforms.
  • Define security, access control, and data privacy standards for analytics data (row-level security, column-level masking, encryption at rest/in transit, role-based access control) in partnership with security and compliance teams.
  • Lead proofs of concept and migrations of legacy data warehouses to modern cloud-native analytics platforms; plan cutovers, data reconciliation, rollback strategies, and post-migration validation.
  • Create and maintain architectural diagrams, technical specifications, and runbooks for analytics solutions, ensuring knowledge is documented and teams can operationalize and support production data products.
  • Partner with product managers and business stakeholders to prioritize analytic initiatives, create roadmaps for data product delivery, and ensure analytics outcomes map to measurable business KPIs.
  • Mentor and coach analytics engineers, BI developers, and data engineers on architectural patterns, code quality, observability, and best practices to raise team capability and consistency.
  • Implement observability, monitoring and alerting for data pipelines and analytic queries (logging, SLA monitoring, lineage-based alerts) to ensure reliability and faster incident resolution.
  • Define and govern the lifecycle of analytical datasets including retention policies, archival strategies, and data purging consistent with legal and regulatory requirements (GDPR, CCPA, HIPAA where applicable).
  • Translate complex analytics requirements into high-level and detailed design artifacts—schema designs, transformation logic, data flow diagrams, and interfaces with ML and operational systems.
  • Work with finance and engineering leadership to model and optimize analytic platform costs, including storage tiers, compute provisioning, and query concurrency limits.
  • Evaluate and introduce new analytics technologies (real-time streaming architectures, OLAP caches, vector search, lakehouse patterns) and partner with platform engineering to pilot and operationalize when beneficial.
  • Drive standards for data quality measurement and remediation (data contracts, monitoring for completeness/accuracy/timeliness) and coordinate with source system owners to resolve persistent issues.
  • Serve as the escalation point for complex analytics incidents, lead root cause analyses, and build preventive measures into architecture and processes.
  • Design and implement integration patterns for third-party analytics tools, embedded analytics solutions, and cross-functional data sharing with external partners while ensuring secure and GDPR-compliant exchange.
  • Advocate for and operationalize self-service analytics adoption by enabling governed sandboxes, standard templates for dashboards, and training materials for business analysts and power users.
  • Collaborate with cloud engineering and DevOps to define infrastructure-as-code patterns, automated provisioning, and environment promotion strategies for analytics environments (dev/test/prod).
  • Influence organizational data strategy by contributing to roadmaps, vendor evaluations, RFP responses, and by translating technical trade-offs into business impact and ROI.

Secondary Functions

  • Support ad-hoc data requests and exploratory data analysis.
  • Contribute to the organization's data strategy and roadmap.
  • Collaborate with business units to translate data needs into engineering requirements.
  • Participate in sprint planning and agile ceremonies within the data engineering team.
  • Provide technical review and architectural approval for analytics-related design documents and code reviews.
  • Facilitate cross-team workshops to standardize metric definitions and reporting taxonomy across departments.
  • Assist in vendor selection and manage relationships with analytics and data platform vendors, including contract and SLA discussions.
  • Design onboarding and enablement programs for new analytics users to accelerate adoption of curated data products.
  • Maintain a prioritized backlog of technical debt items for the analytics platform and lead remediation planning.
  • Represent the analytics architecture team in steering committees, data governance councils, and executive briefings.

Required Skills & Competencies

Hard Skills (Technical)

  • Cloud Data Platforms: Deep hands-on experience with Snowflake, Google BigQuery, AWS Redshift, or Azure Synapse.
  • Data Modeling: Expert-level dimensional modeling, normalized vs. denormalized patterns, and schema design for analytics workloads.
  • ELT/ETL & Analytics Engineering: Expertise with dbt, Airflow/Prefect, Spark, SQL-based transformations, and pipeline orchestration.
  • SQL & Performance Tuning: Advanced SQL, query optimization, indexing/partitioning strategies, and cost-control techniques.
  • Programming: Proficiency in Python and/or Scala for data processing, orchestration, and automation.
  • BI Tools & Semantic Layers: Experience designing for Tableau, Power BI, Looker, Qlik, or ThoughtSpot and implementing semantic layers or governed datasets.
  • Data Governance & Metadata: Familiarity with data cataloging tools (Alation, Collibra, Amundsen), data lineage, and metadata management.
  • Data Security & Compliance: Knowledge of RBAC, row-level security, masking techniques, encryption, and regulatory requirements (GDPR, CCPA, HIPAA).
  • Observability & Data Quality: Implementing monitoring, alerting, SLA enforcement, and data quality frameworks (Great Expectations, custom checks).
  • Real-time & Streaming: Experience integrating streaming data (Kafka, Kinesis, Pub/Sub) into analytics architectures where applicable.
  • DevOps & IaC for Data: Experience with infrastructure-as-code (Terraform, CloudFormation), CI/CD pipelines, and automated deployment for analytics stacks.
  • Architectural Design & Documentation: Ability to produce high-quality architecture diagrams, technical specifications, and runbooks.
  • Cost Management: Skills in cost modeling, budgeting, and optimization of cloud compute/storage for analytics workloads.
  • API & Integration Patterns: Experience building data APIs, data contracts, and secure integrations for internal/external consumers.

Soft Skills

  • Strategic thinking with the ability to translate business goals into a pragmatic analytics roadmap.
  • Strong stakeholder management and communication skills; able to present technical architecture to executives and translate business needs for engineers.
  • Leadership and mentoring aptitude; experience growing team capability and setting technical standards.
  • Problem-solving mindset and decisiveness during incident response and architectural trade-offs.
  • Collaborative and cross-functional: proven track record working with product, engineering, data science, security, and compliance teams.
  • Customer-focused: orientation toward enabling analysts and business users with high-quality, trusted data products.
  • Change management and influence: ability to drive adoption of new analytics practices and platforms across large organizations.
  • Attention to detail coupled with a systems-level perspective to balance immediate needs and long-term maintainability.
  • Time management and prioritization skills in a fast-paced, cross-functional environment.
  • Continuous learner mindset to stay current with evolving analytics, cloud, and data engineering technologies.

Education & Experience

Educational Background

Minimum Education:

  • Bachelor’s degree in Computer Science, Information Systems, Data Science, Engineering, Mathematics, Statistics, or related field.

Preferred Education:

  • Master’s degree or MBA with emphasis on analytics, data engineering, or information systems.
  • Certifications in cloud platforms (Snowflake, Google Cloud, AWS), dbt, or enterprise data governance.

Relevant Fields of Study:

  • Computer Science
  • Data Science / Analytics
  • Information Systems
  • Software Engineering
  • Mathematics / Statistics
  • Business Intelligence / Decision Science

Experience Requirements

Typical Experience Range:

  • 7–12+ years of progressive experience in data engineering, analytics engineering, BI, or data platform roles; at least 3–5 years in architect-level responsibilities.

Preferred:

  • Demonstrated experience architecting and operating analytics platforms at scale in a cloud environment for enterprise customers, implementing governance, and delivering measurable business outcomes.
  • Prior experience leading cross-functional teams, mentoring engineers, and partnering with executive stakeholders on data strategy and roadmaps.
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