Overview

Boston Consulting Group partners with leaders in business and society to tackle their most important challenges and capture their greatest opportunities.

Responsibilities

• 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
• 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
• 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
• 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
• 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 determine what data persists into the unified profile when records merge
• Design for scale and extensibility so new data sources (new martech tools, new regions, new business lines) can be onboarded without re-architecting the core model
• Actively monitor the health, performance, and reliability of BCG's marketing data pipelines and Snowflake environment — establishing the observability frameworks, alerting standards, and incident response protocols that minimize disruption to the analytical products and insight outputs the marketing organization depends on
• Lead the triage and resolution of critical technical incidents with urgency, clarity, and sound engineering judgment — communicating clearly with product owners and stakeholders throughout, and ensuring that root cause analysis and remediation are completed systematically rather than reactively
• Identify and address performance bottlenecks across the data engineering environment — conducting regular performance reviews and implementing optimization improvements that keep the platform scalable and responsive as data volumes, analytical complexity, and user demand grow
• Build strong working relationships with external stakeholders across BCG's global data and technology organization ensuring that marketing's data engineering work is aligned with firm-wide data standards and that dependencies are managed proactively
• Partner with IT architecture and platform teams on shared infrastructure decisions — including AWS cloud services, Azure Active Directory integration, and CI/CD pipeline tooling — ensuring marketing's engineering choices are compatible with BCG's broader technology environment
• Engage with product portfolio management and cross-functional data product teams across finance, HR, legal, and other BCG functions — contributing a marketing analytics perspective to firm-wide data platform conversations and maintaining awareness of cross-functional data and security trends that affect the marketing data environment
• Leading a chapter of data engineers with technical authority and genuine investment in their development — you set a high bar, model it yourself, and bring people along rather than simply holding them to it
• Designing data pipeline architectures and Snowflake data models that are not just technically correct but genuinely built for the analytical consumption patterns of a sophisticated marketing analytics function — you think about the downstream insight need as much as the upstream engineering requirement
• Writing advanced SQL and well-structured dbt models that your colleagues can read, understand, maintain, and build on — you treat code quality and documentation as professional obligations, not optional extras

Qualifications

• 8–10 years of data engineering experience with a strong focus on data warehouse architecture, pipeline development, and data modeling, including at least 2–3 years in a chapter lead, technical lead, or senior engineering leadership role
• Demonstrably advanced SQL proficiency and deep hands-on experience with Snowflake — including data modeling, performance optimization, security configuration, and consumption layer design for analytical workloads
• Expert-level proficiency with dbt for data transformation and data model development, and strong hands-on experience with Apache Airflow for pipeline orchestration and job scheduling
• Solid experience applying SCD, CDC, and DQ/DV frameworks in production data engineering environments — you have built these patterns, maintained them, and debugged them when they break
• Demonstrated ability to write clean, well-documented, version-controlled code — you use GitHub or Bitbucket as a professional standard, not an afterthought
• Experience integrating data from complex, multi-platform marketing or enterprise technology ecosystems — familiarity with platforms such as Dynamics CRM, Adobe Analytics, Marketo, Sprinklr, or equivalent marketing technology is a meaningful advantage
• Working knowledge of AI and machine learning concepts as they apply to data engineering — including intelligent pipeline orchestration, AI-assisted data quality, and the evolving role of Snowflake Cortex or equivalent capabilities in analytics environments
• Strong Python scripting skills for pipeline development, automation, and data engineering tooling; experience with PowerShell or equivalent a plus
• Experience with cloud platforms — AWS preferred, Azure and GCP also valued — and familiarity with CI/CD pipeline tooling and version control workflows in a production data engineering environment
• Experience working in Agile and scrum-based delivery environments — including active participation in sprint ceremonies, backlog grooming, and cross-functional squad collaboration with product owners, architects, and data SMEs
• Excellent written and verbal communication skills — including the ability to present architecture decisions, engineering trade-offs, and technical recommendations to non-technical product owners and marketing stakeholders clearly and confidently
• Experience with JIRA and Confluence for delivery management and technical documentation
• Familiarity with other data platforms including Oracle, SQL Server, or MDM systems is a plus
• Bachelor's degree in Computer Science, Information Science, Data Management, or a related field, or equivalent practical experience