Data Architecture & Data Engineering Leader 

Data Architecture & Data Engineering Leader 

Location: 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.