Looking for Data Architect SME—Remote

Hello,

This is Tejaswini Senior Lead Recruiter from Metasis Information Systems

This is in reference to the following position

Data Architect SME

Job Location:-Remote

Client is looking for a Data Architect SME resource

Note:- Candidate should have Hands on experience in Databricks +AWS, Data Modeling & Design, PySpark Scripts, SQL Knowledge, Unity Catalog and Security Design, Identity federation, Auditing and Observability system tables/API/external tools, Access control / Governance in UC, External locations & storage credentials, Personal tokens & service principals, Metastore & unity catalog concepts, Interactive vs production workflows, Policies & entitlements, Compute types (incl. UC & non UC, scaling, optimization)

1.            Data Strategy & Architecture Development

•              Define and implement the data architecture and data strategy aligned with business goals.

•              Design scalable, cost-effective, and high-performance data solutions using Databricks on AWS, Azure, or GCP.

•              Establish best practices for Lakehouse Architecture and Delta Lake for optimized data storage, processing, and analytics.

2.            Data Engineering & Integration Architect ETL/ELT pipelines leveraging Databricks Spark, Delta Live Tables, and Databricks Workflows.

•              Optimize data ingestion from sources like Oracle Fusion Middleware, Web Methods, MuleSoft, and Informatica into Databricks.

•              Ensure real-time and batch data processing with Apache Spark and Delta Lake.

•              Work on data integration strategies, ensuring seamless connectivity with enterprise systems (e.g., Salesforce, SAP, ERP, CRM).

3.            Data Governance, Security & Compliance Implement data governance frameworks leveraging Unity Catalog for data lineage, metadata management, and access control.

•              Ensure compliance with HIPAA, GDPR, and other regulatory standards in life sciences.

•              Define RBAC (Role-Based Access Control) and enforce data security best practices using Databricks SQL and access policies.

•              Enable data stewardship and ensure data cataloging for self-service data democratization.

4.            Performance Optimization & Cost Management Optimize Databricks compute clusters (DBU usage) for cost efficiency and performance tuning.

•              Define and implement query optimization techniques using Photon Engine, Adaptive Query Execution (AQE), and caching strategies.

•              Monitor Databricks workspace health, job performance, and cost analytics.

5.            AI/ML Enablement & Advanced Analytics Design and support ML pipelines leveraging Databricks ML flow for model tracking and deployment.

•              Enable AI-driven analytics in genomics, drug discovery, and clinical data processing.

•              Collaborate with data scientists to operationalize AI/ML models in Databricks.

6.            Collaboration & Stakeholder Alignment Work with business teams, data engineers, AI/ML teams, and IT leadership to align data strategy with enterprise goals.

•              Collaborate with platform vendors (Databricks, AWS, Azure, GCP, Informatica, Oracle, MuleSoft) for solution architecture and support.

•              Provide technical leadership, conduct PoCs, and drive Databricks adoption across the organization.

7.            Data Democratization & Self-Service Enablement Implement data sharing frameworks for self-service analytics using Databricks SQL and BI integrations (Power BI, Tableau).

•              Promote data literacy and empower business users with self-service analytics.

•              Establish data lineage and cataloging to improve data discoverability and governance.

8.            Migration & Modernization Lead the migration of legacy data platforms (Informatica, Oracle, Hadoop, etc.) to Databricks Lakehouse.

•              Design a roadmap for cloud modernization, ensuring seamless data transition with minimal disruption.

Mandatory Key Skills:

1.            Databricks & Spark Expertise Strong knowledge of Databricks Lakehouse architecture (Delta Lake, Unity Catalog, Photon Engine).

•              Expertise in Apache Spark (PySpark, Scala, SQL) for large-scale data processing.

•              Experience with Databricks SQL and Delta Live Tables (DLT) for real-time and batch processing.

•              Understanding of Databricks Workflows, Job Clusters, and Task Orchestration.

2.            Cloud & Infrastructure Knowledge Hands-on experience with Databricks on AWS, Azure, or GCP (preferred AWS Databricks).

•              Strong understanding of cloud storage (ADLS, S3, GCS) and cloud networking (VPC, IAM, Private Link).

•              Experience with Infrastructure as Code (Terraform, ARM, CloudFormation) for Databricks setup.

3.            Data Modeling & Architecture Expertise in data modeling (Dimensional, Star Schema, Snowflake, Data Vault).

•              Experience with Lakehouse, Data Mesh, and Data Fabric architectures.

•              Knowledge of data partitioning, indexing, caching, and query optimization.

4.            ETL/ELT & Data Integration Experience designing scalable ETL/ELT pipelines using Databricks, Informatica, MuleSoft, or Apache NiFi.

•              Strong knowledge of batch and streaming ingestion (Kafka, Kinesis, Event Hubs, Auto Loader).

•              Expertise in Delta Lake & Change Data Capture (CDC) for real-time updates.

5.            Data Governance & Security Deep understanding of Unity Catalog, RBAC, and ABAC for data access control.

•              Experience with data lineage, metadata management, and compliance (HIPAA, GDPR, SOC 2).

•              Strong skills in data encryption, masking, and role-based access control (RBAC).

6.            Performance Optimization & Cost Management Ability to optimize Databricks clusters (DBU usage, Auto Scaling, Photon Engine) for cost efficiency.

•              Knowledge of query tuning, caching, and performance profiling.

•              Experience monitoring Databricks job performance using Ganglia, CloudWatch, or Azure Monitor.

7.            AI/ML & Advanced Analytics

•              Experience integrating Databricks ML flow for model tracking and deployment.

Knowledge of AI-driven analytics, Genomics, and Drug Discovery in life sciences

Thanks & Regards,

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