Lead Databricks Architect – Financial Services & Banking

About Sutherland:

At Sutherland Healthcare Solutions, we’re transforming how we work by harnessing the power of AI, data, and digital innovation to accelerate the discovery and development of life-saving therapies. Our vision is to embed artificial intelligence and advanced analytics across every stage of our scientific and operational workflows-empowering our teams with smarter tools, seamless data, and actionable insights to deliver client-centric services and solutions that better serve our customers and, ultimately, improve patients’ lives.

Sutherland is seeking an experienced Lead Databricks Architect with deep domain expertise in the banking and financial services industry to drive the strategy, design, and implementation of our enterprise lakehouse platform.

In this role, you will bridge the gap between complex banking business requirements and cutting-edge data architecture. You will be responsible for building a secure, scalable, and highly performant Databricks lakehouse environment that powers critical banking applications, including regulatory reporting, risk management, fraud detection, customer analytics, and real-time transaction processing.

Key Responsibilities

Architecture & Platform Strategy

• Design and implement an enterprise-grade Databricks Lakehouse architecture leveraging Delta Lake, Unity Catalog, and Databricks SQL tailored for financial data models.
• Establish architectural patterns for high-throughput batch and real-time streaming data pipelines (e.g., Structured Streaming, Auto Loader) integrating core banking systems, payment gateways, and third-party data providers.
• Drive the migration strategy from legacy data warehouses (e.g., Teradata, Oracle, Snowflake, mainframe environments) to Databricks.

Security, Compliance & Governance

• Implement strict data security, enterprise access controls, and fine-grained permissions using Databricks Unity Catalog to comply with financial regulations (e.g., GDPR, SOX, PCI-DSS).
• Architect data masking, tokenization, and encryption strategies for Personally Identifiable Information (PII) and Sensitive Personal Data (SPD).
• Ensure full data lineage, auditing, and metadata management across multi-cloud or hybrid-cloud deployment environments (Azure).

Data Engineering & Performance Optimization

• Define best practices for PySpark, Delta Live Tables (DLT), and SQL optimization to maximize compute efficiency and reduce total cost of ownership (TCO).
• Establish CI/CD, DevOps, and MLOps automation frameworks for code deployment, data quality validation (e.g., Great Expectations), and model lifecycle management.
• Partner with Data Science, Quantitative Analytics, and BI teams to support advanced financial modeling, predictive risk analysis, and customer 360 initiatives.

Stakeholder & Domain Leadership

• Collaborate with business leaders, Chief Risk Officers (CROs), and compliance teams to align data platform capabilities with core banking business objectives.

Provide technical leadership, mentorship, and architecture governance to data engineering and analytics teams.

Required Technical Skills

• Databricks Expertise: 5+ years of hands-on experience designing and running large-scale Databricks environments in production.
• Cloud Architecture: Deep knowledge of AWS, Azure, or GCP data services (e.g., Azure Data Lake Storage/Data Factory, AWS S3/Glue, or GCP GCS/BigQuery).
• Data Processing & Storage: Mastery of Delta Lake, PySpark, Databricks SQL, Structured Streaming, and Delta Live Tables.
• Governance & Security: Strong hands-on experience configuring Databricks Unity Catalog, RBAC, ABAC, and data governance frameworks.
• DevOps & Infrastructure as Code: Proficiency with Terraform, CI/CD pipelines (GitHub Actions, Azure DevOps, Jenkins), and Databricks Asset Bundles (DABs).

Required Banking Domain Knowledge

• Domain Experience: 3+ years working within a retail bank, investment bank, or financial services institution.
• Financial Data Models: Familiarity with core banking operations, payments, lending, credit risk, anti-money laundering (AML), or fraud data structures.
• Regulatory Landscape: Strong understanding of banking data compliance requirements, auditability, and data sovereignty standards.

Education & Certifications

• Education: Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Systems, or a related quantitative field (or equivalent practical experience).
• Certifications (Preferred):

  Databricks Certified Data Engineer Professional

  Cloud Solutions Architect Certification (AWS Certified Solutions Architect / Azure Solutions Architect Expert)

All your information will be kept confidential according to EEO guidelines.

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