• Collaborate closely with data scientists, architects, and other stakeholders to understand and implement business requirements • Provide data engineering support for AI model development and deployment, ensuring data scientists have access to the data they need in the format they need it • Implement and optimize data transformations and ETL/ELT processes, using appropriate data engineering tools • Work with a variety of databases and data warehousing solutions to store and retrieve data efficiently • Implement monitoring, troubleshooting, and maintenance procedures for data pipelines to ensure the high quality of data and optimize performance • Participate in the creation and ongoing maintenance of documentation, including data dictionaries, data catalogues, data flow diagrams, and process documentation

• Design the business data model based on the discovered business processes and data analysis • Set the standard for AI-augmented development practices • Translate business requirements into technical design specifications • Develop work estimates for Data Warehouse & Data Lake deliverables • Coach and mentor a team of a few dozen data engineers, analysts and ML Engineers • Define the data architecture framework, standards, and principles • Define reference architecture, which is a pattern others can follow • Define data flows, including management and transitions • Collaborate and coordinate with team members, clients and external SMEs

• 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