Lead Data Architect - Cybersecurity

Lead Data Architect

Duration: 6 Months

Client Location: Juno Beach, FL (Remote)

"Hands-on cyber experience is required."

Role Summary The Lead Data Architect owns the enterprise data architecture strategy and leads design and delivery of scalable, secure, and governed data platforms.

This role partners with business and technology stakeholders to modernize data ecosystems (data lake/warehouse/lakehouse), enable analytics and AI use cases, and ensure data quality, lineage, and compliance across the organization.

Key Responsibilities Architecture & Strategy

Define enterprise data architecture standards, reference architectures, and target-state roadmaps (EDW, lakehouse, data products, streaming, APIs).

Lead architecture decisions for data ingestion, storage, processing, modeling, and serving layers across on-prem and cloud.

Establish patterns for domain-oriented data products, reusable pipelines, metadata, and self-service analytics enablement. Data Platform Design & Delivery

Design and guide implementation of data lake/warehouse solutions (e.g., AWS Redshift/Snowflake/Databricks/BigQuery/Synapse), including performance, partitioning, and cost optimization.

Lead data integration and ETL/ELT architecture using tools like SSIS/Informatica/dbt/ADF/Glue/Airflow.

Own data modeling strategy: conceptual/logical/physical models, dimensional modeling, and canonical models for key domains. Governance, Quality, and Security

Define and enforce data governance: classification, retention, access controls, and stewardship models.

Establish data quality frameworks (rules, controls, monitoring), ensuring trusted datasets and consistent KPIs.

Ensure compliance with security and regulatory requirements (PII/PHI/PCI/SOX/GDPR), including encryption, tokenization, and least privilege access. Stakeholder & Program Leadership

Partner with Product Owners, Engineering, Security, and business leaders to align architecture with strategic outcomes.

Lead architecture reviews, design approvals, and technical decision forums; document ADRs (Architecture Decision Records).

Mentor data engineers/analysts; set engineering standards for code quality, CI/CD, and operational excellence.

Drive vendor evaluation and selection; support contracting, SOW reviews, and implementation oversight.

Required Qualifications

8 12+ years in data architecture / data engineering / enterprise data platform roles, with 2 5+ years leading architecture.

Strong experience designing cloud-scale data platforms (lake/warehouse/lakehouse) and data integration patterns.

Proven expertise in:

o Data modeling (dimensional, normalized, semantic models)

o ETL/ELT and pipeline orchestration

o SQL and performance tuning

Data governance, lineage, and metadata management concepts

Strong understanding of modern data security patterns: IAM/RBAC/ABAC, encryption, key management, and audit logging.

Experience communicating architecture to both technical and non-technical stakeholders; ability to drive consensus.

Preferred Qualifications (Nice to Have)

Experience with Amazon Redshift, Power BI, and enterprise semantic modeling (tabular models, star schemas, DAX optimization).

Exposure to data mesh, domain-based ownership, and data product operating models.

Experience with streaming/event platforms (Kafka/Kinesis) and real-time analytics patterns.

Familiarity with ML/AI enablement (feature stores, model monitoring, governance).

Certifications:

AWS/Google Cloud Platform/Azure data certs, TOGAF (optional), DAMA/CDMP (optional).

Key Deliverables

Enterprise data architecture target state and roadmap

Reference architectures, patterns, standards, and ADRs

Data platform designs (ingestion processing storage serving)

Data models and semantic layer strategy (BI-ready, governed KPIs)

Governance artifacts: classification, lineage, stewardship, access model

Performance/cost optimization recommendations and runbooks Success Metrics

Reduced time to deliver trusted datasets / analytics products

Improved data quality scores and reduction in recurring data defects

Increased adoption of governed datasets and standardized KPIs

Platform performance and cost eAiciency (SLA attainment, optimized spend)

Audit readiness and reduction in compliance findings