Data Architect – Contract | Hybrid

Data Architect – Contract | Hybrid
Experience: 8+ Years
Employment Type: Contractual
Work Mode: Hybrid
Job Description
We are looking for an experienced Data Architect to define and drive the target data architecture for the Horizon MVP and its future evolution. The ideal candidate will have strong expertise in Databricks, AWS, Apache Spark/PySpark, Delta Lake, Unity Catalog, data governance, and modern cloud data platforms.
Key Responsibilities
Define the target data architecture for the Horizon MVP and future platform evolution.
Design end-to-end architecture covering data ingestion, storage, processing, serving, reporting, and downstream API layers.
Establish canonical data schemas for travel signals, corridors, sources, evidence, scores, and generated insights.
Design normalization strategies for structured, semi-structured, and unstructured data from multiple external sources.
Define Databricks architecture, Unity Catalog configuration, schemas, access controls, and governance standards.
Design data retention, lineage, quality, security, and privacy controls.
Define integration patterns across Databricks, AWS PRODIGY, SharePoint, external APIs, and downstream applications.
Design scalable processing for 30-day signal windows, convergence/divergence scoring, corridor ranking, and spike detection.
Ensure the platform is scalable for future vector storage, LLM integration, and multi-year analytics.
Review technical designs and provide architectural guidance to Backend, Data Engineering, DevOps, and QA teams.
Communicate architecture decisions and technical recommendations effectively to technical and business stakeholders.
Mandatory Requirements
8+ years of experience in Data Engineering/Data Architecture, with 3+ years in a Data Architect role.
Strong experience designing cloud-based data platforms, lakehouses, or analytical platforms.
Advanced hands-on experience with:
Databricks
Apache Spark / PySpark
Delta Lake
Unity Catalog
Strong knowledge of AWS data services, IAM, networking, and secure cloud integration.
Expertise in data modeling, metadata management, data lineage, data quality, retention, and governance.
Experience designing batch and API-driven ingestion pipelines for structured and unstructured data.
Understanding of ML/AI workloads, feature pipelines, vector databases, embeddings, and LLM integration patterns.
Experience with API architecture and downstream data-serving patterns.
Strong understanding of PII handling, data privacy, RBAC, and enterprise security controls.
Excellent communication and architecture documentation skills.
Mandatory Skills
Data Architecture | Databricks | Apache Spark | PySpark | Delta Lake | Unity Catalog | AWS | Data Lakehouse | Data Modeling | Data Governance | Data Lineage | Data Quality | ETL/ELT | API Integration | IAM/RBAC | Data Security & Privacy | ML/AI Data Architecture | Vector Databases | LLM Integration

Work arrangement
No