About the Role:

Grade Level (for internal use):

We are seeking a strong technical and people leader to join asDirector of Engineering Data Platformsfor our offshore team in this role you will partner closely with the core Databricks team to support the transition of existing data pipelines to the target platform while continuing to refine enhance and operationalize those pipelines for scale quality reliability and secure cloud-native delivery on AWS. AWS data engineering practices commonly emphasize automated and orchestrated data flows metadata-driven pipelines and alignment to platform guardrails and security controls.

This leader will manage a small focused engineering team responsible for supporting offshore platform adoption and enabling asset-agnostic data onboarding into the enterprise data platform. The role is execution-oriented and collaborative in nature with accountability for high-quality delivery engineering rigor and consistent partnership with global stakeholders.

You will also support integrations with the enterprise data mastering platform to help ensure trusted standardized and reusable data flows across the ecosystem. Experience withNeoXam DataHubis a strong plus particularly in environments where master market reference risk and investment data are managed across the full lifecyclefrom acquisition through distributionto create a trusted single source of truth.

This role is ideal for someone who combines hands-on data platform depth with practical team leadership strong cross-geography collaboration and experience establishing repeatable cloud data engineering patterns across modern lakehouse and mastering ecosystems.

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.
• 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 asAmazon S3for durable storageAWS Gluefor integration and catalog-driven processing andAWS Lake Formationfor governed data lake controls. AWS training and prescriptive guidance for data engineering emphasize building optimizing and securing solutions with these services.
• Promote the use ofAWS 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.

Data Onboarding and Asset-Agnostic Enablement

• 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 asAWS GlueAWS LambdaAmazon 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.

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 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 asNeoXam 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 supportgolden record / golden copygeneration 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.

Team Leadership and Delivery Management

• 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.

Collaboration Governance and Engineering Standards

• Partner effectively with global platform architecture governance and Databricks-aligned teams to ensure offshore delivery aligns with enterprise standards.
• Contribute to design reviews implementation planning and technical discussions related to pipeline architecture onboarding patterns cloud platform controls and mastering integrations.
• Support governance requirements through appropriate controls around lineage schema consistency data quality retention and secure access.
• Promote adoption of engineering best practices including automated testing CI/CD observability incident response and production support readiness.
• Help establish cloud engineering standards forinfrastructure as code release automation and environment promotion using tools and services such asAWS CodePipelineAWS CodeBuild and infrastructure automation frameworks where appropriate.
• Ensure monitoring and operational health practices are in place using logging alerting and telemetry patterns appropriate for enterprise data platforms including AWS-native operational tooling where relevant.

Required Qualifications

Leadership and Delivery Experience

• 10 yearsof experience in data engineering data platforms or related engineering domains.
• 5 yearsof 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.

Technical Expertise

• Strong hands-on experience with modern data engineering and pipeline development including batch and/or streaming data workflows.
• Experience working withDatabricks-based data platformsand supporting migration or transition of pipelines into a lakehouse-oriented architecture.
• Strong familiarity withAWS cloud-native data engineering including services such asAmazon 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 asDelta Lake Apache Iceberg and Databricks Unity Catalog with the ability to apply them in practical engineering delivery contexts.
• Experience supporting data integration patterns involving masteringMDM or trusted data distribution workflows.
• Solid understanding of data quality schema management lineage metadata access control and operational support for production data pipelines.
• Experience withAWS 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 testingCI/CD infrastructure automation and monitoring.
• Experience designing or overseeing data platform observability and reliability practices including alerting monitoring and operational dashboards.

Collaboration and Execution

• 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.

Preferred Qualifications

• 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 masteringMDM reference data market data investment data corporate actions or golden record workflowsin 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 asingle source of truthfor 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.