Aleph - Senior Analytics Engineer
Requirements
• This is a senior analytics engineering role on Aleph's Data team. Our customers integrate diverse systems (financial data, headcount and payroll data, sales and customer data) into Aleph, and we own everything that happens next. We build and maintain the transformations that turn messy raw data into FP&A-ready analytical tables, defining one golden set of transformations across all customers while adding customization where it matters. • We're in an exciting phase of growth, scaling fast on strong foundations. The problems here span building reliable, performant transformation pipelines and data models, and integrating diverse sources at scale. We focus on data integrity, performance, and flexibility, and we lean on AI to ship faster and keep support light. • Experience: A track record of owning transformation pipelines over financial or operational data end to end, from source integration through production-ready models. Experience working with financial data is a plus • Strong fundamentals: you can reason about data from source to consumption and make good technical decisions across the pipeline. Strong SQL and experience with dbt (or similar); comfort with version control, testing, and deployment • Strong fundamentals: • High agency: you drive standards, support and incentivize great practices across the team, and shape architectural decisions. You handle ambiguity without needing constant direction and are the person others trust when things are hard or unclear • High agency: • Craft: you care deeply about code quality and data integrity, building transformation layers that are reliable, performant, and well-documented, and refactoring and optimizing at scale • Craft: • Communication: clear, direct, and persuasive with technical and non-technical audiences, partnering effectively with Customer Success, Engineering, and stakeholders • Communication: • Growth trajectory: you uplevel the team, thrive in a fast-paced environment, coach others, and contribute to hiring and onboarding • You're familiar with our stack or can learn quickly: • Transformations: dbt, SQL, Python • Integrations: many sources (ERP, HRIS, CRMs, billing); ingestion includes Airbyte, Fivetran, Merge • Observability: Datadog • Version control & deployment: Git, CI/CD for dbt • While this is a remote-first opportunity, we're focusing on candidates within the Americas to better align with our working hours as a team.
Responsibilities
• Own how we build client-facing data transformations faster, better, and in a more scalable way as we add integrations and customers, including where and how we leverage AI to accelerate, improve, and document that work • Design and evolve data integrity tests in collaboration with Engineering and Customer Success, so issues are caught before they reach customers • Partner with Customer Success to understand data and reporting needs, troubleshoot issues, and build robust transformations across many sources • Refactor and optimize critical models and pipelines, defining patterns and conventions others follow • Raise the bar through excellent code, thoughtful reviews, and clear communication; improve team processes and developer experience
Benefits
• LATAM$86K – $116K • Offers Equity • US$145K – $192K • Offers Equity • CAN$138K – $169K • Offers Equity • In addition salary ranges listed, total compensation also includes generous equity and benefits. • Fully Remote in the US, CAN, and LATAM • Regular, in-person Retreats • Provided MacBook Pro or Lenovo • Health Insurance (US and CAN Employees) • 401(k) AND RRSP (US and CAN Employees) • Upload your resume here to autofill key application fields. • Drop your resume here! • Parsing your resume. Autofilling key fields... • or drag and drop here • Not Applicable (I do not reside in the United States) • Consultant; built custom models tailored to each client • Built into the product; my models powered a feature customers used • Internal; my models were used by analysts or business teams inside the company • Self-directed; I built the models and used the output myself • Single-source: one system, one customer • Multi-source: one customer, reconciling data across several systems (CRM, ERP, HRIS) • Multi-tenant: same model logic across many customers, each with different schemas • Multi-tenant and multi-source: same model logic across many customers, each reconciling data across several systems • I've built or set up automations or workflows that others on my team also use • I use AI tools regularly to move faster and get more done • I use AI tools occasionally, but they're not central to how I work • Prefer not to answer
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