Associate Director, Data Platforms — Technical Lead

About the Role: Grade Level (for internal use): 12 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 inc