Databrick Architect

E-Solutions; Village of Tarrytown (NY); Sur place; USD 180 000 - 240 000; Plein temps

Résumé du poste

E-Solutions in New York city is seeking an experienced Enterprise Data Architect to lead our Databricks Lakehouse initiatives on AWS. You will architect and govern end-to-end data pipelines, modernize legacy ETL workloads, and drive cloud transformation across large-scale data ecosystems.

The role requires strong skills in Delta Lake, Unity Catalog, PySpark, and data governance, with a focus on scalable, secure, and observable data platforms that support business analytics and AI workloads.

Qualifications

• Experience leading Databricks Lakehouse architecture on AWS.
• Proven track record in data platforms modernization and cloud transformation.
• Strong governance, security, and compliance practices.
• Experience with CI/CD, IaC, and DevOps.

Responsabilités

• Lead enterprise-scale Databricks Lakehouse Architecture design and implementation on AWS.
• Drive large-scale data platform modernisation and cloud transformation initiatives.
• 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 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.

Connaissances

Databricks Lakehouse Platform

Unity Catalog

Databricks Workflows

Auto Loader

PySpark

Spark SQL

Python

AWS

Data Governance

Security

CI/CD

GitHub

Jenkins

Delta Lake

Serverless Compute

Lakehouse Federation

MLflow

Outils

Databricks

Airflow

Lambda

Glue Catalog

Jenkins

Description du poste

• 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
• Unity Catalog
• Databricks Workflows
• Auto Loader
• PySpark, Spark SQL, Python
• AWS (S3, IAM, Glue, Redshift, EMR, Lambda)
• 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.
• 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.