Senior Analytics Engineer
Job description
About the role
This role transforms raw data into reliable, self-serve analytics. The Senior Analytics Engineer connects engineering outputs with business questions and supports data-driven decision-making for clients. Success depends on scalable, trustworthy data foundations and clear communication.
Software engineers turn product ideas into working code. Engineers work in small teams, review each other's work, and ship in small batches. Most teams follow agile practices such as sprints and daily standups. Engineers also write tests, fix bugs, and improve performance. The field values clear communication as much as technical skill. Engineers spend part of every week on planning, code review, and debugging, not just writing new code. The ability to explain a technical decision in plain words separates strong engineers from the rest.
Key facts
What you'll do
Data models and transformation layers are defined and built using dbt and SQL to turn multi-source inputs into reusable, business-friendly datasets.
Requirements from Product, Marketing, Sales, and Finance are gathered, expectations are adjusted when scope evolves, and technical solutions that address business needs are delivered.
Discovery on data tools and infrastructure leads the work, while technical design of schemas and pipelines follows best practices for architecture and quality assurance.
Semantic layers and documentation are created and maintained so data consumers can quickly locate the right datasets for self-service analytics.
Design flaws in data models are identified and corrected, and the end-to-end ecosystem covering ETL/ELT, data governance, and performance is actively managed.
Independent research guides when to seek collaboration and when to drive innovation, balancing external resources with internal experimentation.
Business questions are converted into technical specifications, and findings are shared with non-technical stakeholders.
System design participates in, and guidance on data risks and dependencies is offered to support resilient architectures.
Requirements
The posting states a bachelor's degree requirement. Applied analytics or data engineering experience of 4+ years is expected, preferably within a fast-paced tech company or consulting environment.
SQL skills at an expert level enable writing, troubleshooting, and optimizing complex scripts for performance and cost.
dbt (Data Build Tool) is used to manage the transformation layer of the modern data stack.
Data modeling expertise covers industry concepts such as Star Schema and Snowflake Schema and the construction of semantic layers.
Cloud platforms including GCP, AWS, and Azure, along with cloud warehouses such as Snowflake, BigQuery, or Redshift, are familiar territory.
Data models and measures are defined and refined in BI and visualization tools such as Looker or Tableau.
Practical notes
Work is closely with local colleagues in Buenos Aires offices located in Villa Crespo or Mar del Plata.
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
Data and analytics roles rely on robust data modeling, SQL, and modern data stack tools such as dbt.
Cloud data warehouses and BI platforms are central to analytics workflows in technology and marketing services companies.
B Corp certification indicates a focus on purpose-driven work and social responsibility.
This role supports DEI commitments, including flexible accommodations during recruitment.
Flexible vacation policies and benefit programs may vary by location and employment status.