Director of Engineering, Data Platforms

• Partner with the core Databricks team to plan and execute the transition of existing data pipelines to the target data platform.

• Partner with the core Databricks team to plan and execute the transition of existing data pipelines to the target data platform.
• Lead the offshore team in enhancing, stabilizing, and optimizing pipelines after transition, with a focus on performance, scalability, maintainability, operational excellence, and secure AWS deployment patterns.
• Drive implementation of repeatable engineering patterns for ingestion, transformation, testing, deployment, and monitoring across onboarded datasets.
• Ensure pipelines are built 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 leveraging relevant AWS services such as Amazon S3 for durable storage, AWS Glue for integration and catalog-driven processing, and AWS Lake Formation for governed data lake controls. AWS training and prescriptive guidance for data engineering emphasize building, optimizing, and securing solutions with these services.
• Promote the use of AWS IAM , encryption, and environment-level controls to enforce secure access to platform resources and data products in line with enterprise governance expectations. AWS guidance highlights alignment with architectural guardrails and security controls for data engineering platforms.

• Lead a small team responsible for supporting offshore teams with onboarding data to the enterprise platform in an asset-agnostic manner.

• Lead a small team responsible for supporting offshore teams with onboarding data to the enterprise platform in an asset-agnostic manner.
• Define and operationalize onboarding patterns that can support a broad range of data assets, domains, and source systems without requiring bespoke platform redesign for each use case.
• Work with partner teams to simplify and standardize how data is ingested, transformed, governed, and published to the platform.
• Help offshore teams adopt common onboarding frameworks, technical standards, and delivery practices that improve speed and reduce friction.
• Establish reusable ingestion and processing patterns across batch and streaming use cases using technologies such as AWS Glue , AWS Lambda , Amazon Kinesis , or event-driven integrations where appropriate to support scalable enterprise onboarding. AWS data engineering curricula and roadmap materials consistently position these services as core building blocks for modern cloud data engineering.

• Support integration of platform pipelines and datasets with the enterprise data mastering platform.

• 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 needed 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, and downstream distribution of trusted data products. NeoXam positions DataHub as a configurable enterprise data management platform for the financial industry that manages the full lifecycle of master, market, risk, and investment data.
• Work with business and platform stakeholders to support golden record / golden copy generation and maintenance for key financial datasets, including reference data, instruments, business entities, and related mastered domains. NeoXam DataHub is specifically positioned to provide a single source of truth and has been used in financial services environments for enterprise-level golden copy data management.

• Lead, mentor, and develop a small team of data engineers supporting offshore onboarding and pipeline engineering.

• Lead, mentor, and develop a small team of data engineers supporting offshore onboarding and pipeline engineering.
• Set clear priorities, manage team execution, and ensure delivery commitments are met with quality and accountability.
• Provide day-to-day coaching, technical guidance, and performance feedback to team members.
• Foster a strong engineering culture centered on collaboration, continuous improvement, documentation, operational discipline, and measurable service ownership.
• Drive engineering rigor across sprint delivery, production readiness, support models, and incident management for business-critical data platforms.

• 10+ years of experience in data engineering, data platforms, or related engineering domains.

• 10+ years of experience in data engineering, data platforms, or related engineering domains.
• 5+ years of experience leading engineering teams, preferably in a distributed or offshore delivery model.
• Proven ability to lead a small, high-performing technical team in an execution-focused environment.
• Experience partnering with global or onshore stakeholders to deliver platform and pipeline initiatives across time zones.

• Strong hands-on experience with modern data engineering and pipeline development, including batch and/or streaming data workflows.

• 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 , and related orchestration, security, and monitoring capabilities used to build scalable and governed data platforms. AWS training materials for data engineering specifically emphasize designing, building, optimizing, and securing data solutions using AWS services.
• Working knowledge of technologies and concepts referenced in the source profile, such as Delta Lake, Apache Iceberg, and Databricks Unity Catalog , with the ability to apply them in practical engineering delivery contexts.
• Experience supporting data integration patterns involving mastering, MDM , or trusted data distribution workflows.
• Solid understanding of data quality, schema management, lineage, metadata, access control, and operational support for production data pipelines.
• Experience with AWS IAM , data lake governance, and metadata-driven controls to support secure, policy-aligned platform operations. AWS prescriptive guidance highlights the use of metadata automation, platform guardrails, and security controls in enterprise data engineering.
• Familiarity with engineering best practices such as version control, automated testing, CI/CD , infrastructure automation, and monitoring.
• Experience designing or overseeing data platform observability and reliability practices, including alerting, monitoring, and operational dashboards.

• Strong communication and stakeholder management skills, with the ability to work effectively across offshore and global teams.

• Strong communication and stakeholder management skills, with the ability to work effectively across offshore and global teams.
• Ability to translate broader architectural direction into actionable team deliverables and implementation plans.
• Comfortable operating in a role that blends technical leadership, people management, and hands-on delivery oversight.

• Experience supporting enterprise data onboarding frameworks across multiple business domains or asset types.
• Familiarity with enterprise data cataloging or metadata management environments, including integrations involving operational and business metadata platforms.
• Exposure to data mastering, MDM, reference data, market data, investment data, corporate actions, or golden record workflows in financial services environments.
• Knowledge of NeoXam DataHub is a strong plus , particularly for firms using it to centralize financial data, manage the data lifecycle from acquisition to distribution, enable automated workflows, and deliver a single source of truth for mastered datasets.
• Experience with NeoXam-style mastering workflows, including consolidation, cleansing, enrichment, reconciliation, and trusted downstream distribution of mastered data.
• Experience in regulated or complex enterprise environments where data governance, data controls, and auditability requirements are important.
• Databricks certifications, AWS certifications (such as AWS Certified Data Engineer or AWS Certified Solutions Architect), or equivalent practical platform experience are a plus. AWS certification paths remain relevant for modern cloud-native data engineering roles.