Analytics Engineer

Location

As an Analytics Engineer, you will join the Business Intelligence team within Business Systems Engineering, a group of several teams that builds and runs the systems the company operates on. This role is based remotely in Mexico or Colombia as a full-time employee or in other LATAM locations as a contractor, with working hours that overlap meaningfully with US time zones.

Who We Are

DoiT is a global technology company that works with cloud-driven organizations to leverage the cloud to drive business growth and innovation. We combine data, technology, and human expertise to ensure our customers operate in a well-architected and scalable state - from planning to production.

Delivering DoiT Cloud Intelligence, the only solution that integrates advanced technology with human intelligence, we help our customers solve complex multicloud problems and drive efficiency.

With decades of multicloud experience, we have specializations in Kubernetes, GenAI, CloudOps, and more. An award-winning strategic partner of AWS, Google Cloud, and Microsoft Azure, we work alongside more than 4,000 customers worldwide.

The Opportunity

Our governed data platform is a core part of how DoiT operates, with hundreds of Do’ers relying on it every month. We are growing the team that builds and runs it, and we’re looking for an Analytics Engineer to help carry it forward.

You will work as an engineer on the data platform, not just a builder of reports. You are the kind of person who is averse to doing the same manual task twice, who treats data models like production code, and who instinctively adds tests and monitoring so problems are caught before anyone else sees them. You will own a meaningful share of the platform’s day-to-day engineering, which lets our analysts stay focused on analysis and lets our larger platform initiatives get the sustained attention they need.

This is a high-ownership role with real room to grow. You will work closely with our BI analysts and engineers and with partners across the business, reporting to the Head of Business Intelligence.

Responsibilities

Shared ownership of the platform's engineering : routine model changes, the engineering backlog, new data source onboarding from raw ingestion to certified datasets, CI/CD maintenance, and infrastructure operations. This is a full-ownership role, not a narrow lane.

Build, test, and maintain data models and pipelines that power dashboards, finance and investor reporting, and our AI analytics layer, across 15+ business domains including Finance, Sales, Customer Experience, Marketing, Product, and HR.

Manage data infrastructure as code. Provision and configure BigQuery datasets, views, GCS buckets, and IAM permissions through Terraform. This includes onboarding ingestion destinations, managing access controls, and handling schema drift on source tables.

Own data access and governance. Own and continue expanding our RBAC implementation — extend the access control framework as new domains and consumers come online, keep data catalog metadata (tier, domain, owner, PII tags) current, and keep documentation up to date.

Improve platform reliability by strengthening testing standards, observability, and alerting, so issues are caught early rather than reported by users.

Support orchestration and scheduling. Understand how Airflow DAGs run dbt builds (daily, hourly, monthly), tag models so they land in the right schedule, and troubleshoot build failures in Cloud Composer.

Build and maintain custom data connectors. When a SaaS source isn't covered by a native Fivetran connector, write and maintain Python-based Fivetran SDK connectors — including pagination, rate limiting, error handling, and automated tests.

Partner with BI analysts to turn ambiguous questions into well-modeled, trustworthy datasets, and help raise the team's engineering practices through code review and shared standards.

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