Global M&C Data Architect Director & Chapter Lead – MarTech

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What You'll Do

The MarTech Data Architect & Data Engineering Chapter Lead for Marketing & Communications is a senior technical leader responsible for the engineering excellence, architectural integrity, and continuous evolution of BCG's marketing data foundation and connected platforms. Sitting within the MarTech Engineering team, this person leads the data engineering chapter that serves BCG's Global Marketing analytics and data functions owning the technical standards, pipeline architecture, and data platform capability that underpins the full marketing analytics and data ecosystem, from CRM data operations and audience segmentation through to Tableau dashboard delivery, channel performance intelligence, and AI-assisted insight generation.

A core part of this ownership is the identity resolution model the framework that matches and unifies client and contact records across marketing data and CRM systems into a single trusted profile. The Chapter Lead designs, manages, and continually refines this model, and partners closely with BCG's core data architect, client, marketing, CRM and engineering teams and data functions to maintain a shared, consistent definition of identity across systems.

This role also carries direct responsibility for aligning the marketing data layer with BCG's wider enterprise data approach. The Chapter Lead partners with enterprise data governance and architecture teams to ensure marketing's data standards, access controls, lineage, and quality practices are built to firm-wide policy rather than developed in isolation representing marketing's needs in enterprise governance forums while bringing enterprise requirements back into the marketing data roadmap.

The role sits at the intersection of technical leadership and marketing analytics product delivery. The Chapter Lead is not only the most senior data engineering voice they are an active partner to the product owners, analysts, and insight leaders whose work depends on the reliability, quality, and sophistication of the data infrastructure this person builds and leads. They lead and develop the data engineering team, set the data engineering direction in line witht the overall MarTech strategy and standards, establish the data standards, and ensure that BCG's marketing data platforms are capable of supporting not just today's analytics needs but the agentic intelligent data environment the marketing organization is building toward.

Chapter Leadership & Data Engineering Development

* Lead the data engineering chapter serving BCG's Global Marketing & Communications team setting technical direction, establishing engineering standards, and creating an environment where engineers across squads are developing their skills, doing their best work, and growing their careers
* Provide architectural oversight and hands-on review of engineering work across the chapter code quality, architectural consistency, pipeline reliability, and data modeling standards are upheld across all squads the chapter serves
* Actively mentor and develop chapter engineers conducting regular technical reviews, providing structured feedback, identifying growth opportunities, and building the collective engineering capability that allows the chapter to take on increasingly sophisticated data and AI challenges over time
* Partner with squad product owners to ensure engineering capacity, technical priorities, and sprint commitments are aligned with the analytical products and insight capabilities the marketing organization depends on
* Contribute to hiring decisions, team structure, and data engineering development helping build a chapter that is technically excellent, well-motivated, and capable of sustaining the marketing analytics function's growing ambitions

Data Pipeline Architecture & Engineering

* Design, document, and implement robust, scalable data pipelines that feed the marketing analytics data models powering BCG's dashboard suite, channel insight products, audience intelligence, and CRM data operations built on Snowflake, dbt, and Apache Airflow as the core platform stack
* Build monitoring and alerting for match-rate quality, pipeline failures, and unexpected shifts in merge/split volume
Establish a lineage trail so any resolved identity can be traced back to its contributing source records and the logic that merged them
* Build processes to handle un-merging (splitting incorrectly merged records) as well as merging
* Implement data standardization logic (name normalization, address formatting, email canonicalization) as a pre-processing layer ahead of matching
* Integrate data from across BCG's marketing technology ecosystem into the marketing data environment in a structured, governed, and scalable way that supports both current reporting needs and future analytical capability
* Apply SCD, CDC, and DQ/DV frameworks rigorously across pipeline development ensuring that data arriving in the marketing master dataset is accurate, complete, historically traceable, and fit for the analytical and insight purposes it is intended to serve
* Evaluate and implement new technical solutions, data engineering patterns, and platform capabilities that improve the reliability, performance, and sophistication of BCG's marketing data infrastructure contributing MVPs and proofs of concept that demonstrate feasibility before committing to full implementation

Data Quality, Governance & Security

* Own the data quality and governance standards for BCG's marketing data engineering environment implementing the monitoring, validation, and alerting frameworks that catch data quality issues before they surface as errors in stakeholder-facing dashboards or analytical outputs
* Implement data performance and data security policies that align with BCG's governance objectives, regulatory requirements, and privacy obligations working in close partnership with enterprise data governance, IT security, and legal colleagues to ensure the marketing data environment meets the firm's standards across all dimensions
* Maintain clear, accessible documentation of data models, pipeline architecture, transformation logic, and engineering decisions building institutional knowledge that reduces key-person dependency and supports the chapter's resilience as it grows

AI & Identity Resolution Architecture

* Design the end-to-end identity resolution architecture spanning CRM, marketing automation, web/behavioral data, campaign platforms, and third-party data sources
* Define the matching strategy: deterministic rules (exact matches on email, phone, CRM ID) vs. probabilistic matching (fuzzy name/address matching, behavioral signals), and when each applies
* Design the "golden record" / unified profile structure what fields it contains, how conflicting values are resolved, and which source system wins for each data type
* Build a confidence-scoring framework so matches are tiered (auto-merge, flag-for-review, reject) rather than treated as all-or-nothing
* Define the survivorship rules most recent, most complete, most trusted source that deter

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