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Key Responsibilities and Required Skills for Lead Data Architect

💰 $175,000 - $250,000+

Data & AnalyticsTechnologyArchitectureLeadership

🎯 Role Definition

As our Lead Data Architect, you will be the chief visionary and strategic leader for the company's entire data ecosystem. This is a pivotal role where you will design and implement the blueprint for how we collect, store, manage, and consume data. You will be responsible for translating complex business requirements into a scalable, secure, and high-performance data architecture. Working closely with executive leadership, data engineering, and analytics teams, you will champion best practices in data governance, data quality, and data modeling. Your mission is to build a future-proof data foundation that empowers data-driven decision-making across all levels of the organization and unlocks new opportunities for growth and innovation.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Senior Data Architect
  • Principal Data Engineer
  • Solutions Architect (Data-Focused)
  • Senior BI Architect

Advancement To:

  • Director of Data Architecture / Head of Data
  • Enterprise Architect
  • Chief Data Officer (CDO)
  • VP of Data & Analytics

Lateral Moves:

  • Principal Data Scientist
  • Head of Data Engineering
  • Senior Manager, Data Governance

Core Responsibilities

Primary Functions

  • Design, develop, and maintain the overarching enterprise data architecture, including conceptual, logical, and physical data models for our data warehouse, data lake, and lakehouse environments.
  • Define and drive the execution of the organization's long-term data strategy, vision, and principles, ensuring alignment with executive-level business goals.
  • Lead the evaluation, selection, and implementation of cutting-edge data management technologies, platforms, and tools, including cloud data services, databases, and ETL/ELT solutions.
  • Establish, document, and enforce data architecture standards, policies, and best practices to ensure data integrity, quality, and consistency across the enterprise.
  • Collaborate with C-suite executives, business leaders, and product managers to understand strategic data needs and translate them into robust, scalable, and secure architectural designs.
  • Act as the primary technical leader and mentor for the data engineering and architecture teams, fostering a culture of innovation, collaboration, and technical excellence.
  • Oversee and provide architectural guidance on complex data integration and migration projects, particularly from on-premise legacy systems to modern cloud platforms like AWS, Azure, or GCP.
  • Develop and champion the enterprise-wide data governance and master data management (MDM) framework, partnering with business and compliance teams to ensure data is treated as a strategic asset.
  • Architect and oversee the implementation of real-time data streaming and processing pipelines using technologies such as Kafka, Kinesis, or Spark Streaming to support critical business operations.
  • Create and maintain comprehensive documentation for the data ecosystem, including data flow diagrams, data dictionaries, data lineage, and architectural blueprints.
  • Lead the design and implementation of data security and privacy controls within the data architecture to ensure compliance with regulations like GDPR, CCPA, and HIPAA.
  • Analyze and optimize the performance of data platforms and pipelines, identifying and resolving bottlenecks to ensure efficient and reliable data delivery.
    á- Drive the strategy for data democratization, architecting solutions that enable self-service analytics and BI for business users while maintaining governance and security.
  • Conduct proof-of-concept (POC) projects to assess the viability of new technologies and architectural patterns to address emerging business challenges.
  • Define the architectural patterns for both structured and unstructured data, ensuring the organization can leverage diverse datasets for advanced analytics and machine learning.
  • Provide subject matter expertise and thought leadership on all aspects of data architecture, from data warehousing and big data to cloud infrastructure and data mesh concepts.
  • Lead architectural review sessions and technical design meetings to ensure new projects and features align with the established data strategy and standards.
  • Develop and manage the roadmap for modernizing and evolving the data platform, anticipating future demands and technological shifts.
  • Partner with the DevOps and Infrastructure teams to implement Infrastructure as Code (IaC) practices for data platform components, ensuring automated and repeatable deployments.
  • Manage and resolve architectural debt, creating a strategic plan to refactor and improve the existing data infrastructure over time.

Secondary Functions

  • Support ad-hoc data requests and exploratory data analysis to answer complex business questions.
  • Contribute to the organization's data literacy programs by presenting and evangelizing architectural concepts.
  • Collaborate with business units to translate data needs into engineering requirements and user stories.
  • Participate in sprint planning and agile ceremonies within the data engineering team, providing architectural oversight.
  • Act as a key stakeholder in disaster recovery and business continuity planning for critical data systems.

Required Skills & Competencies

Hard Skills (Technical)

  • Cloud Data Platforms: Deep expertise in one or more major cloud providers (AWS, Azure, GCP), including services like AWS Redshift/S3, Azure Synapse/ADLS, or Google BigQuery/GCS.
  • Data Modeling: Mastery of data modeling techniques, including dimensional modeling (Kimball), 3NF, and Data Vault, for both relational and non-relational data stores.
  • Big Data Technologies: Hands-on experience with the big data ecosystem, including Apache Spark, Hadoop, Presto/Trino, and real-time streaming platforms like Kafka or Kinesis.
  • Data Warehousing & Lakehouse: Proven experience architecting and implementing modern data warehouses and lakehouse platforms (e.g., Snowflake, Databricks).
  • ETL/ELT & Orchestration: Advanced knowledge of data integration patterns and tools, including workflow orchestration frameworks like Apache Airflow, Prefect, or Dagster, and transformation tools like dbt.
  • Database Systems: Comprehensive understanding of various database technologies, including SQL (e.g., PostgreSQL, SQL Server) and NoSQL (e.g., MongoDB, DynamoDB, Cassandra).
  • Data Governance & Cataloging: Experience with data governance frameworks and tools such as Collibra, Alation, or Informatica Axon for metadata management and data lineage.
  • Programming & Scripting: Proficiency in SQL is essential, along with strong skills in a language commonly used in data engineering, such as Python or Scala.
  • Infrastructure as Code (IaC): Familiarity with tools like Terraform or CloudFormation for automating the deployment and management of data infrastructure.
  • API & Data Sharing: Knowledge of designing and implementing secure APIs (REST, GraphQL) for data consumption and sharing.

Soft Skills

  • Strategic Thinking: Ability to see the big picture, align data strategy with business goals, and create a long-term vision.
  • Leadership & Mentorship: Proven ability to lead, inspire, and develop technical teams.
  • Stakeholder Management: Exceptional skill in communicating with, influencing, and managing expectations of both technical and non-technical stakeholders, including C-level executives.
  • Communication & Presentation: Excellent verbal and written communication skills, with the ability to articulate complex technical concepts to diverse audiences.
  • Problem-Solving: Advanced analytical and problem-solving skills to navigate complex architectural challenges.
  • Business Acumen: Strong understanding of business processes and how data can be used to drive value and competitive advantage.
  • Influence & Negotiation: Ability to build consensus and negotiate priorities and trade-offs effectively across different departments.
  • Adaptability: Comfortable working in a fast-paced, evolving technological landscape and adapting strategies as needed.

Education & Experience

Educational Background

Minimum Education:

  • Bachelor's Degree in a quantitative or technical field.

Preferred Education:

  • Master's Degree or PhD in a relevant field.

Relevant Fields of Study:

  • Computer Science
  • Information Systems / Information Technology
  • Software Engineering
  • Statistics or Applied Mathematics

Experience Requirements

Typical Experience Range:

  • 10-15+ years of progressive experience in the data management field, with at least 5-7 years specifically in a data architecture role.

Preferred:

  • Proven track record of designing and delivering enterprise-scale data solutions on a major cloud platform from the ground up.
  • Demonstrable experience leading a team of architects or senior engineers.
  • Extensive experience migrating legacy data warehouses to modern cloud-native architectures.
  • Experience in a specific industry (e.g., Finance, Healthcare, Retail) and its associated data challenges and regulations is a strong plus.
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