Contract: Sr. Analytics Engineer

About the role

The Sr. Analytics Engineer at Newsela owns the design and execution of analytics data structures that power critical business decisions. This position is responsible for transforming complex requirements into scalable data models that stakeholders can trust and use confidently. You will act as a technical leader within the Internal Data Team, ensuring that analytics assets are performant, maintainable, and aligned with product goals. The role requires deep collaboration with cross-functional partners to translate ambiguous problems into clear analytical strategies. You will own the full lifecycle of analytics features, from initial scoping through production deployment and ongoing optimization. Communication is as vital as technical execution, as you must articulate complex data concepts in plain language to non-technical audiences. Success in this role is defined by your ability to deliver reliable insights quickly while maintaining a high standard of code quality and documentation.

Key facts

What you'll do

Design and develop composable data models using SQL to support scalable analytics across the organization.

Optimize SQL query performance to ensure fast, reliable access to large datasets for downstream consumers.

Build and maintain data sets that are clean, organized, and documented for consistent use by analysts and business stakeholders.

Collaborate closely with stakeholders to generate business insights from raw data through defined analysis plans.

Create data visualizations and enable stakeholders to explore available visualization tools effectively.

Implement and refine the business semantic layer to ensure consistent definitions and metrics across all analytics outputs.

Lead data integrity testing frameworks to validate accuracy, reliability, and consistency of analytics pipelines.

Leverage dbt orchestration and best practices to develop robust, production-grade analytics models.

Utilize Python for advanced data transformation, automation, and integration with various data sources.

Monitor and improve DAG tooling performance using systems such as Dagster or Airflow to ensure reliable workflow execution.

Work with document, graph, or schema-less datastores to manage diverse data structures as part of the analytics ecosystem.

Partner with software engineers to integrate analytics into product workflows and ensure seamless data flow across systems.

Champion the adoption of version control and continuous integration practices for analytics code to improve reliability.

Support the expansion of data warehouses by building analysis-ready datasets that drive data-driven decisions.

Requirements

A bachelor's degree is required as part of the eligibility criteria for this position.

Candidates must possess eight or more years of experience working with data in a software environment.

Mastery of SQL and Python is required for data transformation, analysis, and automation tasks.

Advanced experience managing business semantic layer tooling, data catalog tooling, and data integrity testing frameworks is required.

Proficiency with dbt orchestration and best practices is required for model development and deployment.

A track record of working autonomously and proactively is required, along with deep domain knowledge of data systems.

SQL, Python, relational datastores, DAG tooling (such as Dagster or Airflow), dbt, and Tableau are required technologies.

Experience with cloud-based infrastructure, including AWS, GCP, and Terraform, is required for this role.

Experience with document, graph, or schema-less datastores is required to manage unstructured and semi-structured data.

Nice to have

None stated.

Practical notes

This contract role is not eligible for company-sponsored benefits.

Work is conducted remotely across Argentina, Brazil, Chile, and Costa Rica.

Typical interview steps

Hiring for engineering roles usually starts with a recruiter screen, followed by one or two technical rounds. Candidates often solve a coding problem, discuss past projects, and answer system design questions. Some loops include a take-home task. Final rounds typically cover team fit and give candidates a chance to ask questions. Interviewers look for how you break down an unfamiliar problem, not just whether you reach the answer. Practicing a few problems aloud and reviewing your own past projects are the best preparation.

Good to know

Analytics engineers bridge data engineering and analysis using SQL and Python.

Version control and continuous integration help keep analytics code reliable and traceable.

Data warehouses are expanded with clean, analysis-ready datasets.

Advanced testing validates business logic and data quality in ETL or ELT pipelines.

Stakeholders define business logic and data expectations during collaboration.

Insights are surfaced to support data-driven decisions across the organization.

Career growth

Engineering careers usually progress from individual contributor to senior, staff, and principal levels. Some engineers move into management and lead teams of five to twenty people. Others stay on the technical track. Growth follows demonstrated impact, not tenure alone. A typical engineering ladder has clear levels with defined expectations for scope, quality, and mentorship. Moving up usually requires owning outcomes end to end rather than completing assigned tickets.