Quickly build subject matter expertise in a specific business area and data domain.
Understand the data flows from creation, ingestion, transformation, and delivery.
Communicate with engineering teams to fix data gaps for downstream data users.
Take initiative and accountability for fixing issues anywhere in the stack.
Perform reconciliation-style validation across sources, identifying discrepancies and driving fixes.
Interface with stakeholders on data and product teams to deliver the most commercial value from data.
Build curated data models that streamline ledger verification and accounting workflows.
Collaborate cross-functionally with Finance/Accounting to translate requirements into durable data models.

Strong understanding of best practices for designing modular and reusable data models (e.g., star schemas, snowflake schemas).
Proficiency in advanced SQL techniques for data transformation, querying, and optimization.
Expertise in scripting and automation, with experience in Object-Oriented Programming (OOP) and building scalable frameworks.
Strong ability to translate technical concepts into business value for cross-functional stakeholders.
Strong cross-functional communication skills and ability to work effectively with Finance/Accounting partners.
Experience building, maintaining, and optimizing ETL/ELT pipelines, using modern tools like dbt, Airflow, or similar.
Proficiency in building polished dashboards using tools like Looker, Tableau, Superset, or Python visualization libraries (Matplotlib, Plotly).
Familiarity with version control (GitHub), CI/CD, and modern development workflows.
Knowledge of modern data lake/warehouse architectures (e.g., Snowflake, Databricks) and transformation frameworks.
Track record of building for correctness and reliability: data quality frameworks, monitoring/alerting, incident response, and stakeholder-facing SLAs.
Ability to understand and address business challenges through analytics engineering.

Senior Analytics Engineer – Financial Analytics

Key skills