{"id":3429887,"url":"https://alion.io/job/underscore-marketing-data-architecture-data-engineering-leader","title":"Data Architecture & Data Engineering Leader","company":{"id":685095,"name":"Underscore Marketing","domain":"underscoremarketing.com","url":"https://alion.io/company/underscoremarketing","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"lead","employment_type":null,"work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"posting_text","remote_working_hours":{"label":"EST","utc_offset_min":-5,"utc_offset_max":-5},"hiring_geo_confidence":"explicit","locations":["United States"],"countries":["US"],"hiring_countries":["US"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":132000,"max_usd":231000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":127},"experience_years_min":16,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon Redshift","optional":false},{"name":"CI/CD","optional":false},{"name":"Collibra","optional":false},{"name":"Copilot","optional":false},{"name":"Datadog","optional":false},{"name":"dbt","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Fivetran","optional":false},{"name":"GCP","optional":false},{"name":"GDPR","optional":false},{"name":"Gemini","optional":false},{"name":"Google BigQuery","optional":false},{"name":"Great Expectations","optional":false},{"name":"HIPAA","optional":false},{"name":"Incident Management","optional":false},{"name":"Looker","optional":false},{"name":"OpenAI Codex","optional":false},{"name":"Power BI","optional":false},{"name":"Python","optional":false},{"name":"SOC 2","optional":false},{"name":"SQL","optional":false},{"name":"Tableau","optional":false}],"status":"live","first_seen_at":"2026-10-28T23:24:11Z","employer_posted_date":"2026-10-28","last_verified_at":"2026-11-01T17:10:10Z","board_verified":true,"closed_at":null,"days_open":3,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":3},"description":"Data Architecture & Data EngineeringLeader \nLocation: Remote, MUST be located in the United States. \nPrefer candidates who reside in the EST time zone or are willing to work EST hours.\nPosition summary \nWe are looking for a hands-on technical leader to design, build and run the data platform that powers our media, analytics, client reporting and new data products. This role owns the enterprise data architecture and data roadmap, leads data engineering, and builds the data foundation the business needs to launch scalable, marketable products, while making sure our data is secure, compliant, reliable and ready to scale. \nYou will partner with business leaders across Media, Strategy, Client Services and Product to turn business needs into trusted, well-governed data solutions. You will also build and mentor a growing data team, including team members in Kraków, Poland. \nWhat you will do \nData architecture and roadmap \nDefine and own the roadmap for the enterprise data architecture, in line with company priorities and governance. \nDesign the target data architecture, including ingestion, storage, data layers, data models, integrations and the serving layer for reporting and products. \nDocument the architecture, data flows and source mappings so knowledge is shared and doesn't sit with one person. \nSet standards for data modeling, pipeline design, naming conventions, code quality, testing and deployment. \nEvaluate data technologies and vendors, make build-vs-buy recommendations, and manage platform cost and performance. \nData engineering and platform \nOwn the data platform on Google Cloud, including BigQuery, snowflake, redshift etc, data layers, data models and system integrations. \nLead the consolidation of multiple reporting and analytics platforms into a single source of truth. \nDesign and build scalable batch and streaming pipelines from client, vendor, media and first-party sources, using APIs, SFTP and ETL and integration tools. \nLead platform migrations, including moving legacy data sources to BigQuery, snowflake, redshift etc \nStandardize and template recurring data preparation and client data delivery to reduce custom, manual work. \nBuild a well-modeled, well-documented data foundation that supports reporting today and advanced analytics in the future. \nData foundation for new products \nDesign and build a reusable, product-ready data foundation (curated datasets, shared data models and governed views) that new products can be built on without starting from scratch. \nPartner with Product Owners and business leaders to turn new product concepts, such as healthcare, audience and market intelligence offerings, into clear data requirements, data models and delivery plans. \nEvaluate the data feasibility of new product ideas, including source availability, quality, licensing, compliance and cost to build and maintain. \nBuild data services and APIs that deliver product data reliably to client-facing applications, portals and dashboards. \nDesign for scale and multi-client use, with tenant separation, entitlements and configurable data delivery, so one product can serve many clients. \nPartner with Product, Engineering and business stakeholders to align on data contracts, versioning and service levels for product datasets, so products stay stable as the platform evolves. \nHelp identify proprietary and differentiating data assets, and plan how to combine, enrich and package them into new offerings. \nAutomation, data qualityand operations \nAutomate data pipelines end to end, including ingestion, transformation, validation, refreshes and client data feeds, to reduce manual effort and errors. \nUse AI tools and agents to speed up data engineering work, such as generating and testing pipeline code, mapping and onboarding new sources, detecting anomalies and data quality issues, and triaging pipeline failures. \nEstablish data quality, validation and reconciliation frameworks so data is accurate, timely and trusted. \nImplement modern data monitoring and observability tools (for example Monte Carlo, Datadog, Great Expectations or Google Cloud's native monitoring), with automated alerting on freshness, volume, schema changes and data quality. \nSet clear service levels for critical data, and define incident management and root-cause processes for data issues. \nData governance, securityand compliance \nBuild security, privacy and compliance into the data architecture by design, working closely with Infrastructure, Security and Compliance. \nEnsure correct handling of sensitive and regulated data, including healthcare professional (HCP/NPI) data, under applicable regulations and frameworks such as HIPAA, GDPR, CCPA and SOC 2. \nDefine and enforce data governance, including ownership, access controls, data classification, lineage, cataloging, retention and audit trails. \nManage third-party and vendor data risk, including data-sharing agreements and secure transfer standards. \nIdentify, assess and reduce data risk, including key-person dependencies, single points of failure and undocumented systems. \nLeadership and delivery \nBuild, lead and mentor a team of data engineers and analysts, raising technical standards and growing internal talent. \nIntroduce modern engineering practices: version control, CI/CD, code review, documentation, ticketing, sprints and regular release cycles. \nProvide technical direction to internal contributors and external partners. \nTranslate complex data topics into clear recommendations for executives and non-technical stakeholders. \nWhat you bring \nRequired \n16+ years in data engineering or data architecture, including 5+ years leading technical teams. \nProven track record designing and running enterprise data platforms in the cloud, preferably Google Cloud and BigQuery. \nExperience building data foundations that support commercial data products or client-facing platforms, not only internal reporting. \nStrong hands-on skills in SQL and Python, data modeling (dimensional, layered or medallion), and pipeline orchestration and transformation (for example Airflow, Cloud Composer or dbt). \nExperience with data quality, observability and governance tooling and practices. \nSolid understanding of data security, privacy and compliance, with experience handling regulated or sensitive data. \nExperience leading platform migrations and consolidating multiple data sources into a single source of truth. \nAbility to set direction in an organization early in its technical maturity, building structure, documentation and standards from the ground up. \nExcellent communication skills and the ability to work with business stakeholders and lead teams across time zones. \nPreferred \nExperience in media, advertising, marketing technology or healthcare and pharmaceutical marketing. \nFamiliarity with HCP data, NPI matching, media and campaign data, and marketing analytics. \nExperience with data product management practices, data contracts and multi-tenant data delivery. \nFamiliarity with third-party data licensing and data partnership models. \nHands-on experience using AI-assisted development tools and agents (for example GitHub Copilot, Codex or Gemini in BigQuery) in data engineering workflows. \nExperience with data observability platforms and automated data quality testing. \nExperience with BI platforms (Tableau, Power BI, Looker) and ETL and integration tools (Fivetran, Airbyte or similar). \nExperience with data catalog and lineage tools (for example Dataplex, Collibra or Atlan). \nGoogle Cloud Professional Data Engineer or Cloud Architect certification. \nExperience leading distributed or offshore teams, including in Europe. \nEqual Employment Opportunity Statement \nUnderscore is committed to creating a diverse and inclusive workplace. We provideequal employment opportunities to all employees and applicants without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, disability, genetic information, veteran status, or any other protected characteristic under applicable law. 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