Associate Director, Data Platforms Technical Lead

Job Summary Key Responsibilities Data Pipeline Transition and Platform Delivery

• Partner with the core Databricks team to plan and execute the transition of existing data pipelines to the target data platform.
• Drive the implementation of repeatable engineering patterns for ingestion, transformation, testing, deployment, and monitoring across onboarded datasets.
• Ensure pipelines are designed and managed in a way that supports long-term platform consistency, reliability, observability, and ease of support.
• Guide the design and operation of cloud-native data pipelines using AWS services such as:
  • Amazon S3 for durable data lake storage
  • AWS Glue for integration, cataloging, and processing
  • AWS Lambda for event-driven processing
  • Amazon Kinesis for streaming use cases
  • AWS Lake Formation for governed data lake controls
• Promote the use of AWS IAM, encryption, environment-level controls, and platform guardrails to enforce secure access to platform resources and data products.
• Support practical application of lakehouse technologies and concepts such as Delta Lake, Apache Iceberg, Databricks Unity Catalog, metadata-driven pipelines, and governed data access patterns.

Data Onboarding and Asset-Agnostic Enablement

• Define and operationalize onboarding patterns that support a broad range of data assets, domains, and source systems without requiring bespoke platform redesign for each use case.
• Work with platform, data engineering, architecture, governance, and business-aligned teams to simplify and standardize how data is ingested, transformed, governed, and published to the enterprise platform.
• Create or contribute to reusable technical assets such as design patterns, reference implementations, onboarding templates, pipeline frameworks, technical documentation, and operational runbooks.
• Support asset-agnostic onboarding by ensuring data pipelines are configurable, metadata-driven, scalable, and aligned with enterprise data platform standards.

Data Mastering Platform Integration

• Support integration of platform pipelines and datasets with the enterprise data mastering platform.
• Collaborate with upstream and downstream stakeholders to ensure mastered data can be consumed reliably through standardized interfaces and governed data flows.
• Help establish data quality controls, reconciliation processes, metadata alignment, and stewardship workflows required to support trusted mastered data in the platform.
• Contribute to issue resolution and continuous improvement related to mastering-related ingestion and distribution workflows.
• Support data mastering capabilities aligned with platforms such as NeoXam DataHub, including: Data acquisition, Cleansing, Enrichment, Mastering, Reconciliation, Golden copy generation Downstream distribution of trusted data products.

Technical Leadership and Engineering Excellence

• Serve as a senior technical individual contributor for data platform engineering, providing expertise across pipeline migration, lakehouse architecture, AWS-native data engineering, governance, and mastering integrations.
• Influence technical direction without direct people management responsibility.
• Contribute to architecture discussions, design reviews, implementation planning, code reviews, technical standards, and production readiness reviews.
• Translate broader architectural direction into actionable engineering patterns, implementation plans, and technical deliverables.
• Promote engineering best practices including: Version control, Automated testing, CI/CD, Release automation, Monitoring and alerting, Incident response, Documentation.
• Help establish cloud engineering standards for infrastructure automation, release management, and environment promotion using tools and services such as AWS CodePipeline, AWS CodeBuild, and infrastructure automation frameworks.
• Drive operational rigor across production data pipelines, including observability, logging, telemetry, support models, service ownership, and incident management.

Collaboration, Governance, and Platform Standards

• Partner effectively with global platform, architecture, governance, security, Databricks-aligned, and data mastering teams to ensure delivery aligns with enterprise standards.
• Act as a technical bridge between platform strategy and engineering execution.
• Support governance requirements through appropriate controls around:
  • Data lineage
  • Schema consistency
  • Data quality
  • Metadata
  • Retention
  • Access control
  • Encryption
  • Auditability
  • Secure data distribution
• Ensure monitoring and operational health practices are in place using logging, alerting, telemetry, dashboards, and AWS-native operational tooling where appropriate.

Required Qualifications

• 8+ years of experience in data engineering, data platforms, cloud data architecture, or related engineering domains.
• Proven ability to drive technical initiatives and influence engineering outcomes in a complex, execution-focused environment.
• Experience partnering with global or distributed teams to deliver platform and pipeline initiatives across time zones.

Technical Expertise

• Strong hands-on experience with modern data engineering and pipeline development, including batch and/or streaming data workflows.
• Experience working with Databricks-based data platforms and supporting migration or transition of pipelines into a lakehouse-oriented architecture.
• Strong familiarity with AWS cloud-native data engineering, including services such as Amazon S3, AWS Glue, AWS Lambda, AWS Lake Formation, Amazon Kinesis, AWS IAM, and AWS-native monitoring, logging, and security capabilities.
• Working knowledge of technologies and concepts such as Delta Lake, Apache Iceberg, Databricks Unity Catalog, metadata-driven pipelines, data lake governance, lakehouse architecture, and catalog-driven processing.
• Experience supporting data integration patterns involving mastering, MDM, reference data, market data, investment data, risk data, or trusted data distribution workflows.
• Solid understanding of data quality, schema management, lineage, metadata, access control, encryption, and operational support for production data pipelines.
• Experience with AWS IAM, data lake governance, policy-based access controls, and secure platform operations.
• Familiarity with engineering best practices such as version control, automated testing, CI/CD, infrastructure automation, release management, and monitoring.
• Experience designing or implementing data platform observability and reliability practices, including alerting, monitoring, telemetry, operational dashboards, and production support procedures.