Key Responsibilities and Required Skills for Data Architect

🎯 Role Definition

A Data Architect designs, governs, and optimizes an organization's data ecosystem to enable reliable analytics, operational reporting, and ML/AI workflows. This role defines the data architecture strategy, creates reusable data models and patterns, selects and configures cloud and on-prem technologies, enforces data governance and security standards, and partners with engineering, analytics, product, and business stakeholders to deliver high-quality, scalable data solutions.

📈 Career Progression

Typical Career Path

Entry Point From:

Senior Data Engineer with strong system design and modeling experience
Data Warehouse Architect or BI Architect transitioning to platform-level responsibilities
Solutions Architect or Cloud Architect with a focus on data platforms

Advancement To:

Principal Data Architect / Chief Data Architect
Director of Data Engineering or Head of Data Platforms
VP of Data, Analytics, or Machine Learning Infrastructure

Lateral Moves:

Data Engineering Manager
ML Infrastructure Architect / Machine Learning Engineer

Core Responsibilities

Primary Functions

Architect and design end-to-end data platforms (data lakes, data warehouses, lakehouses, and operational data stores) that scale for both batch and streaming workloads, balancing performance, cost, and reliability.
Create and maintain conceptual, logical, and physical data models and canonical data definitions to ensure consistent data semantics across business domains and analytical use cases.
Define and lead the implementation of data ingestion patterns (batch, micro-batch, and real-time streaming) using technologies such as Apache Kafka, AWS Kinesis, Google Pub/Sub, Apache Spark, and Flink, ensuring low-latency, resilient pipelines.
Design ETL/ELT architectures and development standards using tools and frameworks like dbt, Airflow, Glue, Dataflow, Spark, and custom orchestration to enable repeatable, testable, and maintainable pipelines.
Lead cloud data platform selection, design, migration, and optimization on AWS, Azure, or Google Cloud — including Snowflake, Redshift, BigQuery, Databricks, and native cloud services — with an emphasis on cost-efficiency and scalability.
Establish data governance frameworks including data ownership, data lineage, data cataloging, metadata management, master data management (MDM), and stewardship processes to improve discoverability and trust in data assets.
Define and enforce security, access control, encryption, and compliance standards (GDPR, HIPAA, SOC2) across the data platform; implement role-based access, masking, and auditing.
Author and maintain architecture diagrams, solution blueprints, API contracts, and design documents that guide development teams and inform stakeholders about system behavior and constraints.
Drive data quality strategy by specifying validation rules, implementing automated testing and monitoring, and partnering with data engineers to remediate quality issues and maintain SLAs.
Build and enforce performance tuning practices for large-scale analytical systems — including partitioning, clustering, indexing, materialized views, and query optimization — to meet reporting and analytics SLAs.
Own cross-team integration patterns, define canonical APIs and schemas, and create reusable ingestion and transformation components to reduce duplication and accelerate feature delivery.
Lead proof-of-concepts (POCs) and technology evaluations for emerging data technologies (e.g., data mesh, lakehouse architectures, columnar OLAP engines), assessing pros/cons and integration costs.
Define lifecycle management for data retention, archival, and deletion policies to optimize storage costs and meet regulatory requirements while preserving analytical value.
Collaborate with product managers, data scientists, analysts, and business stakeholders to translate business requirements into robust data architecture and roadmap priorities.
Provide technical leadership and code-level reviews for data engineering teams; establish CI/CD pipelines, infrastructure-as-code (Terraform, CloudFormation), and testing practices to reduce delivery risk.
Implement observability, telemetry, and alerting for data pipelines and storage systems (metrics, logs, lineage, SLA dashboards) to enable fast incident response and continuous improvement.
Lead data migration and consolidation projects, ensuring data integrity, mapping of legacy schemas to new models, and phased rollout approaches that minimize business disruption.
Define scalability, high-availability, and disaster recovery strategies for critical data services, including failover plans, multi-region architectures, and capacity planning.
Advocate and operationalize best practices for data modeling (dimensional modeling, normalized vs denormalized patterns), ensuring models support both self-serve analytics and performant downstream processing.
Partner with security, legal, and compliance teams to implement policies around PII/PHI handling, data anonymization, and secure data sharing (tokenization, secure enclaves).
Mentor and upskill data engineering teams on architecture patterns, cloud cost management, and advanced techniques for handling petabyte-scale datasets.

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)

Data modeling: conceptual, logical, physical, dimensional modeling (Kimball) and entity relationship modeling for transactional and analytical systems.
Data warehousing & lakehouse technologies: Snowflake, Amazon Redshift, Google BigQuery, Databricks, Delta Lake.
Big data processing: Apache Spark, Hadoop ecosystem, Dataproc, EMR; experience with PySpark/Scala processing patterns.
Streaming & messaging: Apache Kafka, Kafka Connect, Kinesis, Pub/Sub; designing event-driven, exactly-once or idempotent pipelines.
ETL/ELT orchestration: dbt, Apache Airflow, AWS Glue, Google Cloud Dataflow; building reliable DAGs and transformation pipelines.
SQL mastery: complex query optimization, window functions, set-based processing, explain plans, materialized views and indexing strategies.
Cloud platforms and services: AWS, GCP, or Azure — including S3/GS/Azure Data Lake Storage, IAM, networking, and cost control practices.
Data governance & metadata: data catalog tools (Collibra, Alation, AWS Glue Data Catalog), lineage tracking, data quality frameworks.
Security & compliance: encryption (in-transit and at-rest), RBAC, tokenization, PII/PHI handling, and regulatory compliance (GDPR, HIPAA, SOC2).
Observability & monitoring: Prometheus, Grafana, Datadog, ELK stack, PagerDuty; setting up SLAs and alerts for data pipelines.
Programming languages: Python, SQL, and familiarity with Java/Scala for some big-data components; scripting for automation.
Infrastructure-as-code & CI/CD: Terraform, CloudFormation, GitOps patterns, automated testing for data pipelines.
Performance & cost optimization: partitioning, sorting/clustering, compression, query tuning, storage lifecycle policies, and cloud cost governance.
Data integration & APIs: RESTful API design, data ingestion best practices, CDC (Change Data Capture) patterns using Debezium or native cloud services.
Experience with analytics/BI tools integration: Looker, Tableau, Power BI, ensuring semantic models and governed datasets are consumable.

Soft Skills

Strategic thinking: translate business goals into long-term, scalable data architecture roadmaps.
Stakeholder management: communicate complex technical concepts to non-technical audiences and align priorities cross-functionally.
Leadership and mentorship: lead technical teams, provide constructive reviews, and grow junior engineers.
Problem solving and analytical mindset: diagnose root causes, propose pragmatic trade-offs, and prioritize corrective actions.
Effective communication: produce clear architecture documents, run workshops, and present to executive stakeholders.
Collaboration and negotiation: balance competing needs between product, engineering, security, and compliance teams.
Adaptability and continuous learning: evaluate emerging technologies, adapt architecture decisions as business needs evolve.
Time and project management: deliver on milestones, manage risks, and drive cross-team execution.
Customer focus: empathize with internal data consumers to ensure data products are reliable and easy to use.
Detail orientation: ensure correctness in schema design, documentation, and data lineage to reduce downstream errors.

Education & Experience

Educational Background

Minimum Education:

Bachelor's degree in Computer Science, Information Systems, Software Engineering, Data Science, or a related technical field.

Preferred Education:

Master's degree in Computer Science, Data Engineering, Information Systems, or MBA with strong technical background or equivalent practical experience.

Relevant Fields of Study:

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

Experience Requirements

Typical Experience Range: 5–12+ years in data engineering, analytics engineering, or related technical roles; 3–7 years with architecture responsibilities.

Preferred:

7+ years designing large-scale data platforms, with demonstrable experience in cloud migrations, data modeling at scale, and cross-functional leadership.
Hands-on experience building production ETL/ELT pipelines, managing streaming ingestion, and enforcing governance across multiple data domains.
Proven track record in optimizing cost and performance on cloud data platforms and implementing enterprise-wide data governance.