Data Architect Lead

Role Purpose

Own the end-to-end data architecture strategy for the organization, leading the design of scalable,

secure, and high-performing data platforms. Act as the technical authority on data modeling,

integration, governance, and infrastructure, while leading a team of data architects/engineers and

guiding cross-functional stakeholders on data-driven initiatives.

Key Responsibilities

Architecture & Strategy

• Define and own the enterprise data architecture strategy, standards, and roadmap, aligned with business objectives.

• Design scalable data models (conceptual, logical, physical) across OLTP, OLAP, and data lake/lakehouse environments.

• Architect data pipelines, ETL/ELT processes, and integration patterns across source systems, data warehouses and downstream applications.

• Evaluate and select appropriate data platforms, tools, and technologies (cloud data warehouses, lakehouses, streaming platforms).

• Define and enforce data governance, data quality, master data management (MDM), and metadata management frameworks.

Technical Leadership

• Lead and mentor a team of data architects, data engineers and/or analysts.

• Review and approve data architecture designs, data models, and technical solutions proposed by the team.

• Set technical direction on data security, privacy, access control, and compliance (e.g. GDPR, data residency requirements).

• Drive adoption of best practices in data modeling, naming conventions, version control, and documentation.

Delivery & Collaboration

• Partner with business stakeholders, product owners, and engineering teams to translate business requirements into data architecture solutions.

• Collaborate with BI/Analytics, Data Science, and application teams to ensure data is structured for reporting, analytics and AI/ML use cases.

• Oversee data migration strategies, including legacy system decommissioning and cloud migration projects.

• Support integration projects, ensuring consistent and reliable data flow across systems (ERP, CRM, third-party applications).

• Participate in architecture review boards and provide technical sign-off on major data initiatives.

Governance & Quality

• Establish data quality standards, monitoring, and remediation processes.

• Define and maintain data catalogs, data lineage, and metadata repositories.

• Ensure architecture decisions support scalability, performance, security, and cost optimization.

Requirements

Requirements

• 8-10 years of experience in data architecture, data engineering, or related roles, with at least 3-5 years in a lead/architect capacity.

• Strong hands-on experience designing data models (dimensional modeling, star/snowflake schemas, normalized/denormalized models).

• Deep expertise in at least one major cloud data platform (AWS Redshift/Glue, Azure Synapse/Data Factory, GCP BigQuery/Dataflow, Snowflake, Databricks).

• Strong SQL skills and experience with ETL/ELT tools (Informatica, Talend, dbt, Azure Data Factory, etc.).

• Experience with data warehousing, data lakes, and/or lakehouse architectures.

• Solid understanding of data governance, MDM, data quality, and metadata management principles.

• Experience with API-based and event-driven integration (Kafka, REST, OIC, etc.).

• Strong stakeholder management and ability to translate business needs into technical architecture.

• Experience leading and mentoring technical teams. Desirable

• Experience with BI/reporting tools (Power BI, Tableau, OBIEE, Looker).

• Familiarity with AI/ML data pipeline requirements and feature stores.

• Experience with data security/compliance frameworks (GDPR, HIPAA, data residency).

• Relevant certifications (AWS/Azure/GCP Data Architect, Snowflake, Databricks, DAMA/CDMP).

• Experience in a specific industry vertical relevant to the hiring organization (Finance, Healthcare, Government, Retail).

• Programming experience in Python or Scala for data engineering tasks.

Key Competencies

Strategic thinking, strong technical depth combined with business acumen, leadership and

mentoring ability, structured problem solving, clear communication with both technical and non

technical stakeholders.