• Propose and build: Design modern architectural solutions (focus on Data Lakehouse) and rapidly test new technologies. • Develop: Create efficient data processes and pipelines (ETL/ELT) using industry best practices. • Automate and Orchestrate: Implement automations in Cloud environments (GCP, AWS, or Azure), ensuring high performance, security, and data governance. • Operate with a Product Mindset: Ensure the data platform is scalable, flexible, and oriented to serve business areas with structured data and AI models. • Facilitate and Translate: Act as a bridge between technical and business teams, translating complex engineering and AI concepts for stakeholders in a clear and educational manner. • Collaborate: Support colleagues and teams in adopting industry best practices, serving as a technical reference.

Role Description We are looking for a Mid-to-Senior Data Engineer to architect, build, and operate cloud-native data pipelines on AWS. In this role, you will anchor the flow of data from diverse source systems—including relational databases, SaaS applications, and event streams—into a central Snowflake data platform modeled in dbt Cloud. You will be responsible for ensuring technical excellence across our data infrastructure by writing production-grade Python, utilizing modern AI coding assistants like GitHub Copilot and Claude, and managing infrastructure as code via Terraform. This role is critical to shaping and defending our core data architecture, optimizing platform performance, and delivering curated, analytics-ready datasets for downstream BI and reporting. As a key contributor on a fully remote team, your autonomy, ownership mindset, and clear written communication will directly impact our ability to scale data solutions and maintain full historical accuracy using advanced dimensional modeling techniques. Key Responsibilities - Design and own the architectural evolution of data pipelines and platform components, leading design reviews and defending technical decisions with evidence-based reasoning. - Build and maintain robust ingestion pipelines leveraging AWS DMS, Amazon AppFlow, and Amazon EventBridge to land data seamlessly into AWS S3 and Snowflake. - Optimize and model analytics datasets within dbt Cloud, applying strict dimensional modeling (Kimball techniques) and implementing SCD Type 2 logic. - Lead performance tuning and cost optimization efforts across the Snowflake ecosystem, proactively implementing monitoring, alerting, and root-cause analysis. - Collaborate with cross-functional teams to prepare curated datasets for Power BI, while mentoring peers through rigorous code reviews and engineering enablement. Qualifications - 5+ years of professional experience in Data Engineering roles. - Proficiency with Python (including AWS Lambda), advanced SQL, dbt Cloud, Snowflake, Terraform (IaC), and Docker. - Deep hands-on experience with core AWS data services (S3, Glue, Athena, EventBridge, AppFlow, DMS). - Solid grasp of dimensional modeling (star schemas, fact/dimension design) and practical implementation of SCD Type 2. - High autonomy, ownership mindset, discipline to raise risks early, and comfort utilizing AI-assisted development tools (GitHub Copilot, Claude). - Advanced English (written and spoken) is mandatory for seamless remote collaboration. Nice to Have - AWS Certified Data Engineer – Associate (DEA-C01) or equivalent certification. - Experience with change-data-capture (CDC) and near-real-time ingestion patterns. - Exposure to workflow orchestration tools such as Apache Airflow, Dagster, or Prefect. - Familiarity with data observability frameworks (Great Expectations, Monte Carlo) and open table formats like Apache Iceberg. Benefits - 100% Remote - Holidays off - Paid Time Off - Health insurance assistance - Competitive USD compensation - Growth opportunities

• Design, develop, maintain, and optimize data pipelines that support data integration, transformation, and analytics initiatives • Develop and maintain data models, data warehouses, and data structures that support reporting, business intelligence, and enterprise analytics • Extract, transform, validate, and load data from multiple source systems while ensuring data quality, integrity, consistency, and compliance with business requirements • Support the design, implementation, maintenance, and optimization of the organization’s data architecture and technology platforms • Develop, maintain, and optimize SQL queries, database objects, and data transformation processes to improve performance and scalability • Collaborate with Business Intelligence, Analytics, Technology, and business stakeholders to gather requirements and deliver data solutions that support business objectives • Support data quality initiatives by identifying, troubleshooting, and resolving data inconsistencies, duplicate records, and integration issues • Prepare and maintain technical documentation, data dictionaries, process documentation, and knowledge base resources to support ongoing operations and knowledge transfer • Communicate technical concepts, project updates, and data insights effectively to technical and non-technical stakeholders • Research and evaluate emerging technologies, tools, and best practices to improve data engineering capabilities and operational efficiency • Adhere to company policies and procedures • Meet or exceed performance targets for related KPIs • Continuously improve knowledge of products, services, and processes by participating in training programs and continuous learning modules • Collaborate with other departments as needed • Maintain a positive, empathetic, and professional attitude toward customers and colleagues at all times

• Build and maintain the data models and transformations that power reporting in the Skimmer product, leveraging tools like Fivetran transformations, dbt, and Sigma Computing materializations • Build and maintain customer-facing data models and reports in Sigma Computing, embedded within the Skimmer application • Manage and extend ingestion using Fivetran across a growing set of sources • Develop and maintain data transformations in Python and SQL against our Snowflake data warehouse • Partner with product and engineering to turn customer needs into reliable, performant reporting experiences • Collaborate with application engineers to understand source systems and ensure clean, reliable data capture • Monitor data quality, reliability, and report performance, and troubleshoot issues as they arise • Document data models, definitions, and architecture to support a growing team