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Key Responsibilities and Required Skills for Data Warehouse Architect

💰 $120,000 - $200,000

DataEngineeringArchitecture

🎯 Role Definition

The Data Warehouse Architect is accountable for end-to-end design and delivery of the organization's analytical data platform. This role leads architecture, data modeling, ETL/ELT pipeline strategy, cloud platform selection and optimization, and enforces data governance and security standards. The Architect partners with analytics, data engineering, product, and business stakeholders to translate business requirements into scalable, performant, and cost-effective data warehouse solutions that enable reliable reporting, self-service BI, and advanced analytics.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Senior Data Engineer with proven experience designing data pipelines and dimensional models.
  • Business Intelligence (BI) Architect or Senior BI Developer with enterprise DW experience.
  • Cloud Data Engineer or Analytics Engineer transitioning to platform-level responsibilities.

Advancement To:

  • Head of Data Platforms / Director of Data Engineering.
  • Chief Data Officer (CDO) or VP of Data & Analytics.
  • Enterprise Architect / Solutions Architect with data specialization.

Lateral Moves:

  • Data Engineering Manager
  • Analytics Engineering Lead
  • Machine Learning Infrastructure Architect

Core Responsibilities

Primary Functions

  • Lead the architecture, design, and implementation of the enterprise data warehouse, including logical and physical data models, schema design (star/snowflake), partitioning, indexing, and materialized views to maximize query performance and scalability.
  • Define and enforce data warehousing best practices and architectural standards across ingestion, transformation, storage, and consumption layers to ensure consistency, reusability, and maintainability of data assets.
  • Design and operationalize robust ETL/ELT pipelines using tools and frameworks (Airflow, dbt, Spark, Dataflow, Glue, SSIS) to reliably ingest, cleanse, transform, and enrich data from transactional systems, streaming feeds, and external sources.
  • Evaluate, select, and govern cloud data platforms (Snowflake, Amazon Redshift, Google BigQuery, Azure Synapse) and hybrid architectures based on workload patterns, cost models, security, and organizational goals.
  • Create and maintain long-term data platform roadmaps that balance short-term product needs with strategic investments in performance, automation, observability, and cost optimization.
  • Architect secure data access patterns, encryption-at-rest and in-transit, row- and column-level security, and role-based access controls to meet internal security policies and regulatory compliance (GDPR, HIPAA, SOC2).
  • Partner with data governance and data stewardship teams to implement metadata management, data lineage, cataloging, and classification solutions that improve discoverability and trust in analytical data.
  • Drive performance tuning and capacity planning for the warehouse: analyze query patterns, optimize SQL and execution plans, recommend distribution styles, clustering keys, and compute provisioning strategies to reduce latency and cost.
  • Collaborate with BI and analytics teams to design semantic layers, aggregate tables, and curated data marts that support self-service dashboards, reporting SLAs, and advanced analytics use cases.
  • Implement automated testing, CI/CD pipelines, and quality gates for data models and ETL/ELT code to ensure reproducible, low-risk deployments and rapid iteration.
  • Establish observability and monitoring for the data platform: define KPIs for data freshness, pipeline reliability, SLA adherence, and end-to-end lineage, and implement alerting and runbooks for incident response.
  • Mentor and enable engineering teams by providing architecture runbooks, design patterns, code reviews, and brown-bag sessions to raise cross-team competency in data warehousing technologies and practices.
  • Lead proof-of-concept evaluations for new data technologies (lakehouse, streaming ingest, materialized views, serverless compute) and produce cost-benefit analyses and migration plans as needed.
  • Manage cross-functional delivery by translating business requirements into technical specifications, prioritizing backlog items, and coordinating with product, analytics, and infrastructure teams to ensure on-time, value-driven releases.
  • Define and maintain data retention, archival, and backup/recovery strategies for analytical data, balancing regulatory constraints, storage costs, and query performance needs.
  • Design integration patterns between the data warehouse and other systems — data lakes, operational databases, message buses, and BI tools — to ensure consistent, low-latency data exchange.
  • Drive vendor and licensing evaluations and manage relationships with cloud providers and third-party tooling vendors to negotiate SLAs and optimize spend for compute, storage, and services.
  • Ensure data quality by defining validation rules, implementing anomaly detection, reconciliation jobs, and data-contract enforcement between producers and consumers.
  • Build and document canonical data models and reference architectures for common business domains (sales, finance, marketing, supply chain) to speed new project onboarding and reduce duplication.
  • Champion privacy-by-design and consent management considerations in data schemas and pipelines, implementing pseudonymization or tokenization patterns where required.
  • Coordinate capacity and cost forecasting with FinOps and infrastructure teams to proactively manage budget and rightsizing strategies for compute and storage layers.

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.

Required Skills & Competencies

Hard Skills (Technical)

  • Advanced experience designing and implementing enterprise data warehouses, including dimensional modeling (star, snowflake) and large-scale schema design.
  • Deep SQL expertise: query optimization, window functions, CTEs, explain plans, and complex joins for performance-sensitive analytics.
  • Strong ETL/ELT architecture skills with hands-on experience in tools such as dbt, Apache Airflow, Talend, AWS Glue, Azure Data Factory, or equivalent.
  • Proven experience with cloud data platforms: Snowflake, Amazon Redshift, Google BigQuery, Azure Synapse Analytics — including clustering, partitioning, cost controls, and concurrency handling.
  • Experience with big data processing frameworks like Apache Spark, Flink, or Beam for batch and stream transformations.
  • Familiarity with data lake and lakehouse patterns (Delta Lake, Iceberg), and integration strategies between lakes and warehouses.
  • Experience implementing data governance, metadata management, data catalogs (Alation, Collibra, AWS Glue Data Catalog), and lineage tools.
  • Knowledge of security best practices: IAM, encryption, tokenization, role-based access, network isolation (VPC), and compliance frameworks (GDPR, HIPAA, SOC2).
  • Proficiency with monitoring and observability tooling for data pipelines: Prometheus, Grafana, Datadog, Sentry, or native cloud monitoring.
  • Hands-on experience setting up CI/CD for data engineering (Git workflows, automated testing frameworks, deployment pipelines).
  • Familiarity with containerization and orchestration (Docker, Kubernetes) where data platform components require microservice deployment.
  • Strong automation skills using scripting languages (Python, Bash) and infrastructure-as-code tools (Terraform, CloudFormation).
  • Knowledge of BI and reporting tools (Tableau, Power BI, Looker, Qlik) and how to design semantic models for downstream consumers.
  • Experience designing and enforcing data quality frameworks, monitoring, and automated reconciliation processes.
  • Understanding of cost optimization strategies for cloud storage and compute, including spot/idle compute, auto-scaling, and data tiering.

Soft Skills

  • Strategic thinker capable of translating business priorities into technical roadmaps and measurable outcomes.
  • Excellent communication and stakeholder management skills for aligning technical trade-offs with non-technical leaders.
  • Strong leadership and mentorship ability to grow team capabilities and promote cross-team collaboration.
  • Analytical problem-solver with an eye for detail and a bias for data-driven decision-making.
  • Comfortable operating in ambiguous environments and making pragmatic, documented architecture choices.
  • Project management and prioritization skills to balance short-term delivery with long-term platform health.
  • Collaborative mindset: able to work with data scientists, analysts, engineers, product managers, and security/compliance teams.
  • Change agent who can influence organization-wide adoption of data best practices and tools.

Education & Experience

Educational Background

Minimum Education:

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

Preferred Education:

  • Master’s degree in Computer Science, Data Science, Information Systems, or Business Analytics.
  • Certifications such as SnowPro, Google Professional Data Engineer, AWS Big Data Specialty, or Databricks certifications are a plus.

Relevant Fields of Study:

  • Computer Science
  • Data Engineering / Data Science
  • Information Systems
  • Software Engineering
  • Applied Mathematics / Statistics

Experience Requirements

Typical Experience Range: 6–12+ years in data engineering or analytics roles with 3+ years specifically in data warehouse architecture or platform architecture.

Preferred:

  • Demonstrated history of architecting and delivering enterprise-scale data warehouses in cloud environments.
  • Track record of leading cross-functional teams and driving platform-level decisions that improved performance, cost-efficiency, and time-to-insight.
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