Quick Overview
Job Description
Role : Databrick Architect
Location : Tarrytown New York (Onsite)
Key Responsibilities & Skills
· Lead enterprise-scale data platform modernization initiatives, driving migration from AWS EMR, Apache NiFi, and legacy ETL frameworks to the Databricks Lakehouse Platform.
· Architect and implement scalable Lakehouse solutions using Databricks, Delta Lake, Unity Catalog, and Databricks Workflows.
· Design and govern end-to-end data pipelines for batch, streaming, CDC, and real-time data integration workloads.
· Provide architecture leadership for large-scale AWS-based data ecosystems leveraging S3, IAM, Redshift, Glue Catalog, Airflow, and Databricks.
· Develop enterprise data architecture standards covering data modelling, metadata management, lineage, governance, security, and compliance.
· Drive adoption of Databricks best practices including Delta Live Tables (DLT), Auto Loader, Unity Catalog, Serverless Compute, Lakehouse Federation, and advanced optimization techniques.
· Lead replatforming and migration programmes involving PySpark applications, Airflow DAGs, EMR workloads, Redshift integrations, and NiFi pipelines.
· Partner closely with business stakeholders, enterprise architects, product owners, and data science teams to define technology roadmaps and target architectures.
· Establish governance frameworks using Unity Catalog, data quality controls, observability, monitoring, and operational excellence practices
· Provide technical leadership, mentoring, architecture reviews, design governance, and solution sign-offs across multiple delivery teams.
· Lead architecture workshops, executive presentations, solution assessments, and technology evaluations.
Mandatory Technical Skills
· Databricks Lakehouse Platform
· Delta Lake, Delta Live Tables (DLT)
· Unity Catalog
· Databricks Workflows
· Auto Loader
· PySpark, Spark SQL, Python
· AWS (S3, IAM, Glue, Redshift, EMR, Lambda)
· Apache Airflow
· CDC & Data Migration Frameworks
· Data Governance & Security
· CI/CD, GitHub, Jenkins
JD:
· Lead enterprise-scale Databricks Lakehouse Architecture design and implementation on AWS.
· Drive large-scale data platform modernisation and cloud transformation initiatives.
· Architect scalable Medallion Architecture (Bronze, Silver, Gold) data platforms.
· Lead migration of legacy EMR, NiFi, Redshift, and ETL workloads to Databricks.
· Design high-performance batch and real-time data processing solutions.
· Build robust ingestion frameworks using Auto Loader, Delta Lake, and Structured Streaming.
· Define enterprise data governance and security standards using Unity Catalog.
· Architect metadata-driven and reusable PySpark-based ETL/ELT frameworks.
· Establish best practices for performance tuning, scalability, and cost optimisation.
· Design and implement data quality, lineage, and observability frameworks.
· Drive adoption of CI/CD, DevOps, Infrastructure as Code, and automation practices.
· Collaborate with business, analytics, and engineering teams to define target-state architectures.
· Conduct architecture reviews and provide technical leadership across multiple projects.
· Mentor architects and senior engineers on Databricks and AWS best practices.
· Design secure and scalable solutions leveraging AWS services (S3, Glue, Athena, Lambda, IAM, Redshift).
· Implement and govern enterprise-wide data access, compliance, and security controls.
· Evaluate and adopt latest Databricks capabilities such as DLT, Lakehouse Federation, Serverless, and MLflow.
· Enable AI/ML, Generative AI, and advanced analytics use cases on the Lakehouse platform.
· Create architecture roadmaps, migration strategies, standards, and governance frameworks.
· Act as the primary technical advisor for customer leadership on data strategy and platform evolution.
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