Implementation Data Engineer

This role is open to candidates based in LATAM, Africa, and Eastern Europe. Please note that as this role supports U.S.-based clients, candidates must be available to work during U.S. business hours aligned with the client’s time zone.
 
Client Overview
Our client is a fast-growing AI-powered marketing intelligence platform that helps brands and retailers make smarter media investment decisions through advanced forecasting and attribution modeling. Their engineering team operates at the intersection of data infrastructure and machine learning, building the pipelines and models that power real commercial decisions for some of the most recognizable names in retail and ecommerce.

They move quickly, hold high standards for code quality, and give engineers the autonomy to own their work end-to-end — from the first line of code to a clean handoff with full documentation.

Role Overview
The Implementation Data Engineer is the dedicated technical owner for new client onboarding, responsible for building the customer-specific ELT pipelines, dbt models, and Dagster orchestration that transform a freshly-signed account into a fully modeled, analytics-ready environment. This role sits at the core of the client delivery workflow, partnering closely with the Implementation Project Manager while maintaining full ownership of the technical build. The Implementation Data Engineer works within a modern analytics engineering stack and is expected to deliver independently, surface blockers early, and hand off completed onboardings with clean documentation and monitoring in place.

Location
Fully Remote (Work from Home) | 9AM - 5PM EST
 
Key Responsibilities
Client Onboarding & Pipeline Development
Build and maintain scalable, fault-tolerant ELT pipelines for new client onboarding using Python

Develop and optimize dbt models, tests, and documentation following analytics engineering best practices

Orchestrate and monitor onboarding workflows using Dagster

Model clean, analytics-ready datasets for BI, forecasting, and ML feature consumption

 
Data Quality & Observability
Implement and maintain data quality checks and testing strategies throughout the onboarding lifecycle

Troubleshoot pipeline failures, performance issues, and data inconsistencies during onboarding

Monitor pipeline health using observability tools and metrics

 
Cross-Functional Collaboration
Partner with the Implementation Project Manager to provide realistic engineering ETAs, surface blockers early, and keep work visible in Jira

Collaborate with Data Science to ensure forecasting and AI feature data lands correctly and on time

Write clear, concise status updates that stakeholders can act on without rewriting

 
Handoff, Documentation & Continuous Improvement
Hand off completed onboardings to the core Data Engineering team with documentation, runbooks, and monitoring in place

Support data source re-authentications, migrations, and net-new API additions on live accounts

Contribute to refactoring and improvement of onboarding pipeline templates and patterns as the platform evolves

Follow established team standards for SLAs, code quality, and deployments

Qualifications — Experience
3+ years of professional experience in data engineering or analytics engineering

Strong proficiency in Python (e.g., pandas, SQLAlchemy, psycopg2)

Hands-on experience with dbt (Core or Cloud)

Hands-on experience with Dagster or similar orchestration tools

Advanced SQL skills including CTEs, window functions, and query optimization

Experience with cloud data warehouses such as Snowflake, BigQuery, or Redshift

Familiarity with modern ELT tools such as Airbyte, Fivetran, Meltano, or dltHub

Experience working cross-functionally with Product, Analytics, or Data Science teams

Ability to work independently and deliver consistently in a contract environment

Qualifications — Skills
Strong written communication skills — able to produce status updates clear enough for non-technical stakeholders to act on directly

Proactive about surfacing blockers, data quality issues, and timeline risks before they escalate

Detail-oriented with a bias for clean handoffs, thorough documentation, and maintainable code

Comfortable using AI-assisted tools (e.g., Claude, ChatGPT) to accelerate personal workflow

Self-directed with strong ownership mentality in a fully remote, async-friendly environment

Opportunity
This is a high-impact contract role where your work directly determines how quickly new clients go from signed contract to fully operational analytics — making you a critical part of a product that informs real media spend decisions at scale. You'll work with a modern, well-designed stack (Python, dbt, Dagster) alongside a team that values engineering craft, clear communication, and continuous improvement. If you thrive in implementation-heavy environments, enjoy owning the full technical lifecycle of a project, and want visibility into how AI-driven forecasting products are built and delivered, this is the role for you.
Application Process:
To be considered for this role these steps need to be followed:
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