• Elicit and understand business needs and translate them into technical solutions • Build and optimize data pipelines (ETL/ELT) using SQL and Python • Create advanced, interactive Power BI dashboards with strong visual impact and optimized performance • Develop and automate processes using Power Apps, Power Automate, and Power Virtual Agents • Work on complex projects, integrating Power Platform with APIs, Azure, databases, and legacy systems • Ensure governance, security, versioning, and documentation of the solutions developed • Support the team in promoting BI, data engineering, and automation best practices.

Role Description The Principal Data Engineer is a senior technical authority responsible for defining Boldin’s data architecture, setting long-term technical strategy, and tackling our most complex data engineering challenges. This role shapes company-wide data standards, and partners with executive and cross-functional leaders to ensure our data platform scales with the business. - Define and evolve long-term data architecture and vision - Design resilient and scalable data platform and pipelines - Set standards for data modeling, reliability, observability, and governance - Lead complex, high-risk technical initiatives and migrations - Influence tool selection, and technology adoption across the data stack - Elevate engineering excellence - Partner with leadership to align data strategy and business goals - Enable analytics, ML, and product use cases KPIS + Targets: - Uptime: Consistently meets SLA for business-critical pipelines - Freshness: All Tier 1 datasets delivered within SLA - Delivery predictability: Majority of sprint commitments completed as planned - Cost optimization: Year-over-year efficiency improvement as data scales - Documentation: Full coverage for all production-grade assets Qualifications - Bachelor's degree in Computer Science, Engineering, or related field (or equivalent experience) - 10+ years of experience in data engineering or related disciplines - Proficient in SQL, Python, or related languages - Cloud Platforms (AWS, GCP) - Strong experience with data warehouse, data lakes, and distributed systems - Strong experience with modern data stack (e.g., Athena, BigQuery, Glue, Spark, Dataproc, Kafka, Flink, dbt, Kestra, Fivetran or equivalent) - Proven ability to build and maintain production-grade ELT/ETL pipelines - Experience with workflow orchestration (e.g., Airflow, Dagster, Prefect, Cloud Composer or equivalent) - Experience implementing data quality and observability frameworks - Performance and cost optimization in cloud warehouses - Experience supporting product analytics and experimentation - Ability to translate business requirements into scalable data models - Strong ownership and accountability for SLAs Requirements - Experience working with Kubernetes - Experience structuring data for ML or AI use cases - Familiarity with Amplitude or product event pipelines - Experience in a high-growth SaaS or fintech environment - Influencing technical direction without direct managerial authority Benefits - Salary Range: $180,000 - $220,000/annual DOE - Inclusive hiring process - Encouragement for applications from individuals of all backgrounds - Commitment to providing reasonable accommodations for applicants with differing abilities - Fostering an environment where everyone can bring their authentic selves to work

• Join our Data Engineering team and process, transform, and refine terabytes of TV and advertising data every day. • Design and build scalable data pipelines for our TV and forecasting products • Keep pipelines fast, clean, and reliable – from ETL to testing and deployment • Automate wherever possible – from unit tests to CI/CD processes • Collaborate closely with other engineers to solve complex data challenges and continuously improve our platform • Explore new tools and frameworks – stay curious and keep experimenting

• Develop batch and real-time data pipelines on our GCP platform • Develop APIs to share our data with other teams and consumers • Build CI/CD pipelines for automated deployments • Develop Terraform IAC for deploying our team's core infrastructure • Use Datastream to ingest CDC data to our data lake • Use Dataform and dbt to build a medallion data architecture • Optimize our BigQuery and ClickHouse data models • Build Airflow DAG pipelines for data imports and exports • Implement monitoring and alerting policies for data accuracy, consistency, and integrity across platforms • Uphold GDPR/CCPA compliance and data governance standards