Lead Architect

Fractal Analytics•New York, NY

1d•$150,000 - $200,000

About The Position

We are looking for a Lead Architect – Azure & AI to join our growing data engineering practice. In this role, you will architect and deliver large-scale, cloud-native data solutions on Azure and Snowflake, enabling advanced analytics, ML/AI workloads, and enterprise data governance. You will work closely with cross-functional stakeholders to design resilient, scalable data platforms that drive business impact for Fortune 500 clients.

Requirements

8+ years of experience as a Data Engineer with deep expertise in Azure Data Services (ADF, Synapse, Databricks) and Snowflake.
Strong background in the Insurance domain with hands-on Azure Data Engineering experience (ADB + PySpark/Python + Snowflake).
Proficiency in SQL (complex queries, stored procedures, functions, views) and Python/PySpark programming.
Proven experience designing and implementing ETL/ELT pipelines with Azure Data Factory and orchestrating complex, multi-step workflows.
Comprehensive Snowflake expertise: Star/Snowflake schema design, role-based security, performance tuning, query optimization, and pipeline features (Snowpipe, Tasks, Streams).
Solid understanding of Spark architecture with hands-on experience in performance tuning, data partitioning, and distributed processing.
Experience integrating data from RDBMS, SFTP, APIs, Kafka, Event Hub, and big-data platforms (Hadoop, Hive).
Familiarity with NoSQL database design (Cosmos DB or equivalent) and streaming data pipeline patterns.
Knowledge of ML/AI data preparation workflows; ability to refactor traditional ML code for distributed execution on Spark/Databricks.
Strong command of Agile methodologies, DevOps practices, and CI/CD deployment frameworks.

Nice To Haves

Exposure to data search and indexing technologies such as Elasticsearch or Azure Cognitive Search.
Azure Data Engineer Associate certification (DP-200/201/203) or SnowPro Core/Advanced Architect certification.
Experience working on data platforms serving analytics and AI use cases in large enterprise environments.

Responsibilities

Design, build, and optimize end-to-end data pipelines leveraging Azure Data Factory, Databricks, and Snowflake for large-scale data ingestion, transformation, and loading.
Develop and maintain ETL/ELT workflows for structured, semi-structured (JSON, XML, Parquet), and unstructured data from on-premises systems, cloud platforms, APIs, flat files, RDBMS, and streaming sources.
Write efficient, production-grade code in Python (PySpark) and SQL for advanced data transformations, workflow orchestration, and automation.
Architect scalable Snowflake solutions including schema design, Virtual Warehouse configuration, query performance tuning, and cost-efficient data storage and retrieval strategies.
Leverage Azure Synapse and Snowflake for advanced analytics and data warehouse solutions, including MPP (Massively Parallel Processing) architectures.
Partner with business and technology stakeholders to design DataMart and Data Lake solutions supporting analytics, reporting, and ML/AI use cases.
Implement data quality frameworks, governance policies, lineage tracking, and metadata management across multiple environments.
Optimize Spark workloads on Databricks for distributed processing at scale; deploy and manage reusable Notebooks and Jobs in production.
Integrate NoSQL stores (Cosmos DB, MongoDB, etc.) and build hybrid solutions spanning Snowflake and Azure services.
Leverage Azure Cognitive Search and Elasticsearch for indexing and unstructured data search integration.
Drive DevOps best practices including CI/CD pipelines, source control (Git), and automated deployments via Azure DevOps and Jenkins.
Ensure adherence to enterprise data security, cataloging, and access-control policies across all data assets.