Data Engineer

About Zoe

Zoe is an end-to-end wealth platform that helps clients grow and protect their wealth through innovative technology and exceptional client service. Zoe has raised a total of $45M in venture capital and is backed by Sageview Capital and the Opportunity Fund. In addition, former and current operators from JP Morgan, BlackRock, Charles Schwab, Uber, and DoorDash are part of the cap table.

Its accolades include Nerdwallet’s 2022, 2023, and 2024 Best Online Financial Advisor, Morningstar’s Fintech Startup of the Year 2019, ThinkAdvisor Luminaries’ 2024 Industry Disruption Firm Award, and 2025 FinTech Breakthrough Award for Best Wealth Management Product.

The New York-based company has a strong leadership team with over 20 years of industry experience at firms like Morgan Stanley, JP Morgan, Merrill Lynch, Principal, and Learnvest. We offer the excitement of a rapidly growing company with the stability of a tenured leadership team and board. We have offices in New York and Bogota. 

About the Role

We are looking for an experienced Data Engineer to join the Zoe team in our Bogotá office (Zona Chicó). We need a motivated professional with a strong background in building and scaling data pipelines and a passion for turning scattered sources into a reliable, usable data foundation. As a Data Engineer, you will thrive in a fast-paced environment and take ownership of our data warehouse from start to end.

The ideal candidate will design and implement robust transformations, define tables and schemas with stakeholders, bring strong testing and quality standards experience, and care about monitoring, alerting, and visibility of pipelines and processes, as well as proposing best practices and constant improvements. You care about data quality, lineage, and developer workflows. Using Python, SQL, and modern CI/CD, you will help translate business and analytics needs into a strong data architecture that supports both reporting and AI/ML use cases.

Location: Bogota, Colombia

Reports to: VP of Engineering

Level: Mid-level

We’re excited about you because…

• You put data reliability and clarity first. You think in terms of repeatable pipelines, strong testing, quality standards, and documentation.
• You’re a motivated, high-energy self-starter who is comfortable building structure from scratch, improving existing systems, and proactively proposing best practices and constant improvements.
• You’re curious about the “why” behind the data and enjoy collaborating with analytics, product, and engineering to shape tables and metrics.
• You have strong attention to detail, testing, and quality standards (unit tests, validation, profiling), and take ownership of data contracts, naming, and incremental design.
• You work well in cross-functional teams and can explain technical tradeoffs to non-engineers.
• You care about observability (monitoring, alerting, and visibility of pipelines and processes) so that issues are caught early, and status is clear to stakeholders.

You'll love working at Zoe because we…

• Are a successful, well-funded, fast-growing company with a start-up work vibe.
• Are passionate about our clients and live/breathe the client experience.
• We hire A players. So you will be surrounded by the ‘Navy Seals’ of their craft that will push you to improve
• Are a technologically and data-driven business.
• Offer competitive salaries and equity.
• Are at the forefront of tech & finance, redefining personal finance.
• Believe in autonomy & take the initiative.

Responsibilities

• Design, build, and maintain ETL/ELT pipelines feeding a warehouse, using Python and no-code/low-code tools.
• Ingest and model data from diverse sources (e.g. databases, CRM, Google Analytics, SFTP) with clear schemas and incremental strategies.
• Advance our medallion architecture into silver and gold layers: define transformations, tables, and columns in collaboration with analytics and product.
• Contribute to a dedicated data-warehouse repo with formatters, pipeline checks, code review, and automated deployments; define and uphold testing and quality standards for pipelines and data models.
• Document pipelines, data dictionaries, and lineage to support self-serve analytics and future AI/ML use cases. Support the design and provisioning of data assets for AI features (e.g. vectors, training datasets, model-improvement workflows) as the program scales.
• Help establish patterns for data quality, monitoring, and governance on the infrastructure.
• Implement and maintain monitoring, alerting, and general visibility of pipelines and all data processes so that failures and delays are detected quickly, and status is transparent.
• Propose and adopt best practices and drive constant improvements in tooling, patterns, documentation, and ways of working across the data practice.

Qualifications

• 3+ years of experience in data engineering or a closely related role (e.g. analytics engineering, backend with heavy data workflows experience).
• Strong proficiency in Python and SQL for building pipelines, transformations, and data models.
• Strong experience with testing and quality standards for data pipelines (e.g. unit tests, integration tests, data validation, quality checks, profiling) and a track record of embedding them in CI/CD.
• Experience with orchestration and ingestion tools (e.g. Airflow, dbt, or equivalents) and with version control and CI/CD (e.g. Git, unit tests, automated deployments).
• Experience integrating multiple data sources (databases, APIs, files, SaaS tools) and designing incremental and idempotent pipelines.
• Comfort designing: fact/dimension modeling, aggregations, and working with stakeholders to define metrics and tables.
• Experience implementing monitoring, alerting, and visibility for data pipelines and processes (e.g. dashboards, alerts, run-status visibility, SLA tracking).
• Track record of proposing and implementing best practices and driving constant improvements in data engineering (tools, patterns, documentation, processes).
• Strong communication and collaboration skills; able to work with data, product, and engineering teams in a dynamic environment.
• Proficient in verbal and written English.
• Nice to Have
• Background in data science or data analysis (e.g. reporting, dashboards, ad-hoc analysis).
• Experience with cloud infrastructure (AWS, GCP, Azure).
• Exposure to ML/LLMs or data-for-AI workflows: stores, embeddings/vectors, training datasets, or evaluation pipelines.
• Experience in fintech or regulated environments (e.g. auditability, lineage, access controls).

Benefits

• Competitive base salary.
• Healthcare & Dental coverage.
• Commuting & Gym benefits.
• Lunch every day at the office.
• Opportunity to shape the culture of a fast-growing company as an early team member.
• Annual company offsite in New York City for our Colombia-based team — travel expenses covered!

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