Top Data Architect Jobs in NYC, NY

Designs and governs enterprise data architecture supporting VHA EHR modernization, reporting, analytics, clinical systems, and data integration. Responsibilities include data modeling, governance, metadata and lineage management, APIs, ETL, interoperability, data quality, cloud infrastructure, performance optimization, and stakeholder collaboration. The role also leads architectural enhancements through Agile processes and evaluates emerging data technologies.

Leads enterprise data architecture strategy, modernization, and multi-year platform roadmaps. Defines standards for data models, ingestion, transformation, orchestration, quality, governance, observability, and consumption across batch and streaming environments. Provides technical leadership to data engineering and analytics teams, oversees scalable data solutions, mentors technical leaders, and partners with business, engineering, security, and data science stakeholders. Evaluates technologies, guides vendor selection, enables data products, and explores GenAI applications.

Design and own a unified data model for an AI-driven pipeline risk platform. Responsibilities include lakehouse and medallion architecture, geospatial modeling, entity resolution, ingestion patterns, governance, lineage, data quality, access controls, and traceability. Partner with data engineers, application engineers, and data scientists to implement schemas, pipelines, serving layers, and optimization strategies while maintaining clear architecture documentation.

Design and implement scalable, secure enterprise data solutions on Databricks and Azure using Delta Lake and Medallion Architecture. Lead and mentor data engineering teams; define integration patterns, governance, security (Unity Catalog, RBAC, masking, row/column-level security), and CI/CD/IaC practices. Ensure data quality, lineage, monitoring, and optimize Spark/Delta workloads for performance and cost while aligning solutions with business needs.

Designs and maintains scalable data architecture, Snowflake warehouses, ETL/ELT pipelines, dimensional data models, and governance processes. Partners with business stakeholders to deliver insights for pricing, inventory, customer segmentation, and marketing optimization. Builds reliable analytics datasets and dashboards, integrates data tools, monitors data quality, and ensures compliance within the regulated cannabis industry.

Lead the architecture and implementation of enterprise-scale GCP data ecosystems, including PB-scale platforms and AWS-to-GCP migrations. Establish data platform roadmaps, self-service architectures, governance, security, quality, and Infrastructure as Code standards. Architect solutions using GCP data, analytics, AI, streaming, and containerization technologies. Lead cross-functional engineering teams, mentor staff, advise executives, manage technical risks, and communicate the business value of data initiatives.

Design, architect, and build batch, real-time, and event-driven data platforms and streaming applications. Integrate monitoring and IT operations tools, create data schemas and models, mentor peers, engage in pre-sales, and provide technical leadership and documentation for enterprise data solutions.

Own end-to-end data platform architecture across ingestion, lakehouse, warehouse, transformation, modeling, governance, and analytics delivery. Build pipelines, Spark and SQL transformations, data models, and production code. Design Databricks or Microsoft Fabric lakehouses, implement Delta Lake and medallion architecture, establish access controls and governance, and integrate databases, SaaS applications, APIs, files, and legacy systems. Evaluate platform choices based on client workloads, licensing, skills, and cost.

The Data Architect will utilize data modeling tools, design data lakes, and ensure proper tuning of NoSQL models while managing object design and deployment scripting.

The role involves reverse engineering and optimizing complex database environments, analyzing database schemas, designing data models for efficiency, and administering enterprise databases.

Lead enterprise Master Data Management strategy, architecture, governance, data quality, and integration initiatives across customer, product, supplier, employee, and financial domains. Design data models, hierarchies, matching and survivorship rules, integration patterns, and implementation roadmaps. Advise clients on Oracle and Salesforce ecosystems, lead workshops and executive presentations, guide solution delivery, mentor consultants, and contribute to proposals and statements of work.

Designs and evolves enterprise data architecture across master data governance, MDM, data warehousing, and analytics. The role establishes governance policies, stewardship models, data quality metrics, golden-record strategies, integration patterns, data lineage, and warehouse models. It supports SQL Server and Microsoft Fabric platforms, integrates ERP, CPQ, CRM, and other business systems, advises on tooling and architecture decisions, and collaborates with business and technical stakeholders across a multi-site manufacturing organization.

Designs enterprise-scale federated data architectures integrating Palantir Foundry and Databricks. Establishes data-sharing, governance, lineage, metadata, and ownership standards across cloud platforms. Leads legacy ETL modernization, builds scalable PySpark and Spark SQL pipelines, architects secure connectivity, and enables AI, machine learning, real-time analytics, and self-service data products. Serves as the technical authority, leads architecture reviews, and manages stakeholder relationships.

Own the end-to-end architecture and roadmap for MyFitnessPal’s product data stack. Coordinate data platform contributions across engineering, analytics, and BI teams; establish data movement, standardization, governance, security, and compliance practices; evaluate and consolidate tools; manage access, licensing, and spend; and drive architecture standards, documentation, and adoption across the organization.

Designs and leads enterprise Snowflake data architectures, cloud data platforms, ELT/ETL pipelines, and AI-powered applications. Builds RAG, semantic search, Cortex Agents, vector embedding, and Cortex AI Function solutions using Snowpark, Python, SQL, and dbt. Establishes secure, governed, and cost-optimized data environments, mentors engineering teams, leads architecture reviews, and partners with stakeholders on scalable data and generative AI solutions.

Support and architect clients' Azure data platforms and Power BI environments within Managed Services. Design Databricks Lakehouse and Delta Lake solutions, build and maintain data pipelines, advise on governance/licensing, resolve escalations, mentor junior staff, communicate with clients, create documentation, and promote best practices for HIPAA-compliant healthcare data solutions.

The Data Architect will design scalable data models, transform raw data into actionable insights, and deploy analytics solutions for healthcare providers, with a strong emphasis on SQL proficiency and cloud technologies.

Lead the architecture and implementation of enterprise-scale Azure data and AI platforms, including data lakes, lakehouses, warehouses, integrations, machine learning, and generative AI solutions. Establish governance, security, MLOps, and AI frameworks; guide cloud modernization, migration, performance, and cost optimization. Translate business needs into technical architectures, present recommendations to executives, lead architecture reviews, support client pursuits, and mentor engineering teams.

The Sr. Manager of Data Engineering & Analytics will define data strategy, oversee a data team, and build scalable data platforms, ensuring data quality and governance.

Lead enterprise data architecture across multiple SaaS products by unifying reporting platforms, data models, and analytics capabilities. Assess current systems, define target-state architecture, establish data governance and quality standards, separate analytical workloads from transactional systems, and develop phased migration roadmaps. Guide database, engineering, and BI teams on cloud platforms, integration, modeling, performance, security, and reliability. Enable trusted cross-product reporting, governed self-service analytics, and data foundations for AI and advanced analytics.

Customer-facing technical leader who architects, builds, and optimizes large-scale data engineering and lakehouse solutions on Databricks. Supports pre-sales and delivery with POCs, performance/load testing, workload sizing, migrations, observability, and adoption through workshops and community engagement.

Design and architect robust ETL/data pipelines on cloud platforms. Build and automate data ingestion and preprocessing using Spark/Databricks, AWS Glue, Airflow. Implement warehousing architectures (EDW, DM, ODS, MOLAP/ROLAP), ensure SQL-based data quality, use version control, and deliver tested production pipelines while communicating with stakeholders.

Lead ER/Studio data architecture and modeling efforts: build/maintain macros, standardize domains and metadata, synchronize models with physical databases, enforce naming/versioning standards, document and support modelers and DB engineering for BI and data integration.

Design, build, and support a data lake: manage ingestion, data management, and client consumption. Implement Big Data platforms (HDFS, YARN), optimize Hive (partitioning, reducer/block sizing), develop Spark jobs (Python/Scala), work with ETL tools (Informatica) and NoSQL (Cassandra, Drill, Impala) in AWS. Meet deadlines, handle multiple tasks, and communicate with stakeholders.

Design, build, and support data lake/data management, ingestion, and client consumption. Require strong Big Data fundamentals (HDFS, YARN, Hive tuning), solid Spark (Python/Scala) skills, AWS cloud experience, ETL tooling (Informatica) knowledge, and familiarity with NoSQL tools like Cassandra, Drill, and Impala.