Analytics Engineer

Till Financial•New York, NY

2d•$105,000 - $150,000•Hybrid

About The Position

Till is hiring an Analytics Engineer to take on a growing share of analysis and reporting and build the dbt models that support it. The position is the first data hire alongside the Data Analytics Lead and will help shape Till’s growing data function, including its tools, infrastructure, and future hiring. The role focuses first on marketing measurement, supporting attribution, conversion, and engagement as Till increases its marketing investment. It also partners with lifecycle marketing, product, engineering, and operations to answer business questions with reliable data and actionable recommendations. The Analytics Engineer will build and maintain reporting across the data warehouse and product analytics, ensure customer and event data flows reliably into the customer engagement platform, and develop tested, documented dbt models and dashboards. The role reports to the Analytics Lead and is based at Till’s DUMBO, Brooklyn office, with a few in-person days each week; candidates should be in the greater NYC area. The first 30 days focus on learning the data, models, and dashboards and mapping marketing measurement gaps. By 60 days, core attribution and conversion reporting should be live and CEP data reliable. By 90 days, the marketing measurement framework should be in place and used for spend and program decisions. The base salary range is $105,000 to $150,000 depending on experience, with eligibility for an annual bonus.

Requirements

Bachelor’s degree in a quantitative, analytical, or technical field, or commensurate work experience.
2–5 years of experience in analytics engineering, data analytics, or a similar role; fintech or financial services experience is ideal.
Strong SQL skills, including production models and exploratory queries.
Experience building models in dbt, SQLMesh, or a similar tool, and using git for version control and code review.
Experience with a cloud data warehouse such as Snowflake, BigQuery, or Redshift.
Experience with a BI tool such as Metabase, Looker, Tableau, or Mode, and a product analytics tool such as Mixpanel or Amplitude.
Experience with marketing data, including attribution, conversion, and engagement, and supporting at least one other function.
Working proficiency in Python for analysis and light scripting.
Ability to clearly communicate findings to technical and non-technical audiences and connect them to business decisions.
Authorization to work in the United States; employment visa sponsorship is not available.

Nice To Haves

Experience getting data into a customer engagement platform such as Braze or Iterable, and supporting segmentation and campaign measurement.
Experience using Meta or Google Ads data to measure campaign performance and acquisition cost, or building reporting in Google Analytics.
Experience designing A/B tests and analyzing results.
Experience analyzing retention, churn, or revenue for a subscription product.
Experience building workflows in Airflow or another orchestration tool.
Experience using AI tools to work faster while exercising judgment about when to lead with one's own analysis.

Responsibilities

Build marketing reporting on acquisition, attribution, conversion, and engagement across the warehouse and product analytics.
Maintain the measurement framework that guides acquisition spending and lifecycle programs.
Ensure relevant customer and event data flows reliably into the customer engagement platform, and help the team understand the data.
Translate open-ended requests into analysis grounded in the product and business, and develop recommendations about what to do next and why.
Investigate anomalies and open questions proactively.
Write efficient, tested, and documented dbt models in Snowflake.
Build ad hoc reports and durable Metabase dashboards for teams across the company.
Share findings in an appropriate format, such as a dashboard, document, deck, or spreadsheet, tailored to the audience and business goal.
Provide input on data tools, infrastructure, and future hires as the data function grows.