data engineer

As a Senior Data/Analytics Engineer, you’ll sit at the intersection of data engineering and BI. You’ll own dbt models in Snowflake end-to-end and build the Tableau reporting on top of them. You’ll have real influence over how portfolio, client, and financial data is modeled and presented across the business. The project involves the development and expansion of a centralized, scalable data ecosystem.

• Design, build, and maintain dbt Core projects: layered models (staging, intermediate, marts), incremental models, snapshots, macros, and reusable Jinja logic.
• Own data quality through dbt tests, documentation, and lineage, and keep the project clean, versioned, and CI-ready.
• Design dimensional and domain-specific data models for portfolio, client, and financial reporting.
• Build and maintain data pipelines for ingestion into Snowflake, including API-based sources.
• Orchestrate data workflows using Prefect.
• Integrate and normalize data from SaaS platforms (Addepar, NetSuite, Salesforce, Morningstar, UKG) using Workato.
• Develop and maintain Tableau dashboards and published data sources on top of the dbt marts, and keep them performant and consistent.
• Optimize Snowflake performance and costs (warehouse sizing, clustering, query tuning).
• Work directly with business stakeholders to turn reporting needs into data models and dashboards.

• 5+ years of experience in data engineering or analytics engineering, building modern cloud data platforms.
• Strong, hands-on dbt Core experience in production: model design, incremental strategies, snapshots, macros/Jinja, testing, documentation, and package usage.
• Hands-on experience with Snowflake, including performance and cost optimization.
• Advanced SQL and solid data modeling skills (dimensional modeling, star schemas).
• Hands-on experience building Tableau dashboards and data sources (calculated fields, LOD expressions, extracts vs. live connections, performance tuning).
• Proven experience with ETL/ELT patterns and API-based ingestion.
• Practical experience with orchestration tools such as Prefect or Airflow.
• Solid understanding of financial or wealth management data structures.
• Будет плюсом: dbt certification (dbt Analytics Engineering Certification); Experience within the Wealth Management or Investment Management sector, with specific familiarity handling investment and portfolio-related data; Experience with Workato or similar iPaaS tools; Direct experience with Addepar, Morningstar, or Bloomberg data; Python for data engineering tasks; CI/CD for dbt projects (e.g., GitHub Actions, Slim CI); Understanding of regulatory requirements around financial data handling and privacy

• This position requires availability aligned with the EST time zone, or at least a 4-hour overlap with EST working hours (until 1 pm EST in the EU and APAC regions).