{"id":3446349,"url":"https://alion.io/job/boston-consulting-group-director-chapter-lead-data-architecture-engineering","title":"Director & Chapter Lead - Data Architecture & Engineering","company":{"id":655,"name":"Boston Consulting Group","domain":"bcg.com","url":"https://alion.io/company/bcg-x","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Phenom","truth_index":null},"role":"Leadership","role_family":"Leadership","seniority":"head","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Gurgaon, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":60000,"max_usd":122000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":50},"experience_years_min":13,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Active Directory","optional":false},{"name":"Agile","optional":false},{"name":"Airflow","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Bitbucket","optional":false},{"name":"CI/CD","optional":false},{"name":"Confluence","optional":false},{"name":"Cortex","optional":false},{"name":"dbt","optional":false},{"name":"GCP","optional":false},{"name":"GitHub","optional":false},{"name":"Incident Management","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Master Data Management","optional":false},{"name":"PowerShell","optional":false},{"name":"Python","optional":false},{"name":"Scrum","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false},{"name":"Tableau","optional":false},{"name":"Prometheus","optional":true}],"status":"live","first_seen_at":"2026-10-29T07:28:54Z","employer_posted_date":null,"last_verified_at":"2026-10-29T07:28:54Z","board_verified":false,"closed_at":null,"days_open":6,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":6},"description":"Who We Are : \n\nBoston Consulting Group partners with leaders in business and society to tackle their most important challenges and capture their greatest opportunities.\n\nBCG was the pioneer in business strategy when it was founded in 1963.\n\nToday, we help clients with total transformation - inspiring complex change, enabling organizations to grow, building competitive advantage, and driving bottom-line impact.\n\nTo succeed, organizations must blend digital and human capabilities.\n\nOur diverse, global teams bring deep industry and functional expertise and a range of perspectives to spark change.\n\nBCG delivers solutions through leading-edge management consulting along with technology and design, corporate and digital ventures - and business purpose.\n\nWe work in a uniquely collaborative model across the firm and throughout all levels of the client organization, generating results that allow our clients to thrive.\n\nWhat You'll Do : \n\nThe 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.\n\nSitting 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.\n\nA 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.\n\nThe 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.\n\nThis role also carries direct responsibility for aligning the marketing data layer with BCG's wider enterprise data approach.\n\nThe 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.\n\nThe role sits at the intersection of technical leadership and marketing analytics product delivery.\n\nThe 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.\n\nThey lead and develop the data engineering team, set the data engineering direction in line with 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.\n\nChapter Leadership & Data Engineering Development : \n\n- 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\n\n- 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\n\n- 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\n\n- 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\n\n- 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\n\nData Pipeline Architecture & Engineering : \n\n- 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\n\n- Build monitoring and alerting for match-rate quality, pipeline failures, and unexpected shifts in merge/split volume\n\n- Establish a lineage trail so any resolved identity can be traced back to its contributing source records and the logic that merged them\n\n- Build processes to handle un-merging (splitting incorrectly merged records) as well as merging\n\n- Implement data standardization logic (name normalization, address formatting, email canonicalization) as a pre-processing layer ahead of matching\n\n- 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\n\n- 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\n\n- 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\n\nData Quality, Governance & Security : \n\n- 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\n\n- 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\n\n- 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\n\nAI & Identity Resolution Architecture : \n\n- Design the end-to-end identity resolution architecture spanning CRM, marketing automation, web/behavioral data, campaign platforms, and third-party data sources\n\n- 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\n\n- 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\n\n- Build a confidence-scoring framework so matches are tiered (auto-merge, flag-for-review, reject) rather than treated as all-or-nothing\n\n- Define the survivorship rules most recent, most complete, most trusted source that determine what data persists into the unified profile when records merge\n\n- 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\n\nTechnical Monitoring & Incident Management : \n\n- 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\n\n- 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\n\n- 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\n\nCross-Functional & Stakeholder Partnership : \n\n- 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\n\n- 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\n\n- 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\n\nYOU'RE GOOD AT : \n\n- 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\n\n- 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\n\n- 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\n\n- Navigating the intersection of data engineering and marketing analytics with enough domain fluency to engage credibly with product owners, analysts, and insight leaders - you understand what they are trying to build and why, and you engineer solutions that serve those needs rather than just satisfying technical requirements\n\n- Bringing a current and practical perspective on AI and its implications for data engineering - you are not evangelizing AI for its own sake, but you have a well-formed view of where intelligent automation, Snowflake Cortex, and LLM integration create genuine engineering opportunity in a marketing analytics context\n\n- Staying calm and effective in critical incidents - you triage with sound judgment, communicate clearly under pressure, and resolve issues systematically rather than reactively\n\n- Collaborating with product owners and Agile teams in a way that adds engineering value to product decisions - you show up to sprint ceremonies prepared, you flag technical risk early, and you help the squad make better product decisions by making the engineerin...","description_format":"text","description_chars":17783,"description_truncated":true,"requirements":{"experience_years_min":13,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":["Growth 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