Quick overview
SeniorityMid Senior
Work modeOn Site
LocationTarrytown, NY, United States
SQLGitApache SparkAWSETLMLflowAirflowApacheComplianceDatabricksEMRJenkinsPythonRedshiftUnityGenerative AICI/CDDelta LakeIAM
Job description
Role : Databricks Architect
Location : Tarrytown New York (Onsite)
- Evaluate and drive adoption of new Databricks capabilities, including Lakebase, Databricks Apps, Lakehouse Federation, Serverless, Genie and AI/ML capabilities.
- Conduct architecture reviews, define reusable patterns and provide technical governance across projects.
- Partner with business, engineering, and architecture teams to define target-state architectures and modernization roadmaps.
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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