Data Architecture & Data Engineering LeaderLocation: Remote, MUST be located in the United States.
Prefer candidates who reside in the EST time zone or are willing to work EST hours.
Position summary We 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.
You 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.
What you will do Data architecture and roadmap - Define and own the roadmap for the enterprise data architecture, in line with company priorities and governance.
- Design the target data architecture, including ingestion, storage, data layers, data models, integrations and the serving layer for reporting and products.
- Document the architecture, data flows and source mappings so knowledge is shared and doesn't sit with one person.
- Set standards for data modeling, pipeline design, naming conventions, code quality, testing and deployment.
- Evaluate data technologies and vendors, make build-vs-buy recommendations, and manage platform cost and performance.
Data engineering and platform - Own the data platform on Google Cloud, including BigQuery, snowflake, redshift etc, data layers, data models and system integrations.
- Lead the consolidation of multiple reporting and analytics platforms into a single source of truth.
- Design and build scalable batch and streaming pipelines from client, vendor, media and first-party sources, using APIs, SFTP and ETL and integration tools.
- Lead platform migrations, including moving legacy data sources to BigQuery, snowflake, redshift etc
- Standardize and template recurring data preparation and client data delivery to reduce custom, manual work.
- Build a well-modeled, well-documented data foundation that supports reporting today and advanced analytics in the future.
Data foundation for new products - Design 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.
- Partner 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.
- Evaluate the data feasibility of new product ideas, including source availability, quality, licensing, compliance and cost to build and maintain.
- Build data services and APIs that deliver product data reliably to client-facing applications, portals and dashboards.
- Design for scale and multi-client use, with tenant separation, entitlements and configurable data delivery, so one product can serve many clients.
- Partner 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.
- Help identify proprietary and differentiating data assets, and plan how to combine, enrich and package them into new offerings.
Automation, data quality and operations - Automate data pipelines end to end, including ingestion, transformation, validation, refreshes and client data feeds, to reduce manual effort and errors.
- Use 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.
- Establish data quality, validation and reconciliation frameworks so data is accurate, timely and trusted.
- Implement 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.
- Set clear service levels for critical data, and define incident management and root-cause processes for data issues.
Data governance, security and compliance - Build security, privacy and compliance into the data architecture by design, working closely with Infrastructure, Security and Compliance.
- Ensure 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.
- Define and enforce data governance, including ownership, access controls, data classification, lineage, cataloging, retention and audit trails.
- Manage third-party and vendor data risk, including data-sharing agreements and secure transfer standards.
- Identify, assess and reduce data risk, including key-person dependencies, single points of failure and undocumented systems.
Leadership and delivery - Build, lead and mentor a team of data engineers and analysts, raising technical standards and growing internal talent.
- Introduce modern engineering practices: version control, CI/CD, code review, documentation, ticketing, sprints and regular release cycles.
- Provide technical direction to internal contributors and external partners.
- Translate complex data topics into clear recommendations for executives and non-technical stakeholders.
What you bring Required - 16+ years in data engineering or data architecture, including 5+ years leading technical teams.
- Proven track record designing and running enterprise data platforms in the cloud, preferably Google Cloud and BigQuery.
- Experience building data foundations that support commercial data products or client-facing platforms, not only internal reporting.
- Strong 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).
- Experience with data quality, observability and governance tooling and practices.
- Solid understanding of data security, privacy and compliance, with experience handling regulated or sensitive data.
- Experience leading platform migrations and consolidating multiple data sources into a single source of truth.
- Ability to set direction in an organization early in its technical maturity, building structure, documentation and standards from the ground up.
- Excellent communication skills and the ability to work with business stakeholders and lead teams across time zones.
Preferred - Experience in media, advertising, marketing technology or healthcare and pharmaceutical marketing.
- Familiarity with HCP data, NPI matching, media and campaign data, and marketing analytics.
- Experience with data product management practices, data contracts and multi-tenant data delivery.
- Familiarity with third-party data licensing and data partnership models.
- Hands-on experience using AI-assisted development tools and agents (for example GitHub Copilot, Codex or Gemini in BigQuery) in data engineering workflows.
- Experience with data observability platforms and automated data quality testing.
- Experience with BI platforms (Tableau, Power BI, Looker) and ETL and integration tools (Fivetran, Airbyte or similar).
- Experience with data catalog and lineage tools (for example Dataplex, Collibra or Atlan).
- Google Cloud Professional Data Engineer or Cloud Architect certification.
- Experience leading distributed or offshore teams, including in Europe.
