Azure & AI Data Architect Lead for Enterprise Platforms

Fractal

New York (NY)

On-site

USD 150,000 - 200,000

Full time

14 days+

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Benefits offered by this job

Health insurance

401(k) plan

Parental Leave

Paid holidays

Job summary

Fractal is seeking a Lead Architect – Azure & AI to design and deliver large-scale, cloud-native data solutions on Azure and Snowflake. You will architect data platforms, enable ML/AI workloads, and govern data for Fortune 500 clients.

The role focuses on end-to-end pipeline development, governance, and scalable architecture. You will work with cross-functional stakeholders to implement DataLakes/DataMarts, optimize Spark workloads, and drive DevOps practices in a fast-paced enterprise

Qualifications

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.
Experience designing and implementing ETL/ELT pipelines with Azure Data Factory and orchestrating complex multi-step workflows.
Snowflake expertise: Star/Snowflake schema design, security, performance tuning, and pipeline features (Snowpipe, Tasks, Streams).
Knowledge of Spark architecture with distributed processing and performance tuning.
Experience with data from RDBMS, APIs, Kafka/Event Hub, and big-data platforms; familiarity with NoSQL and streaming patterns.
Awareness of ML/AI data preparation for distributed execution on Spark/Databricks.

Responsibilities

Design, build, and optimize end-to-end data pipelines leveraging Azure Data Factory, Databricks, and Snowflake for large-scale ingestion, transformation, and loading.
Develop and maintain ETL/ELT workflows for structured, semi-structured, and unstructured data from diverse sources.
Write production-grade Python (PySpark) and SQL for data transformations and orchestration.
Architect scalable Snowflake solutions including schema design and cost-efficient storage/retrieval strategies.
Leverage Azure Synapse and Snowflake for advanced analytics and data warehousing; implement DataMarts and Data Lakes.
Establish data quality, governance, lineage, and metadata management across environments.
Implement DevOps practices with CI/CD pipelines and automated deployments; ensure data security and access controls.