Analytics Engineer
DoiT
Salary undisclosed
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Description
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.
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.
- 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.
- Use modern AI tooling to draft code and accelerate routine work, amplifying your output rather than adding manual effort.
- Analytics engineering experience. 3–5 years in analytics engineering, data engineering, or BI engineering, with a track record of delivering data models that other people depend on.
- Strong SQL and data modeling. You write, read, and optimize complex analytical queries, reason about grain and joins, and design clean, layered models with sound judgment about where logic belongs (staging, business logic, consumption layer).
- dbt / Data transformation proficiency. You've built and maintained dbt projects professionally — packages, macros, incremental strategies, snapshots, seeds, and CI/CD. You've implemented staging → intermediate → mart patterns and understand deferred builds, state comparison, and slim CI.
- Software engineering discipline. Version control, code review, automated testing, and CI/CD, with the habit of making changes safe to ship. You have a strong drive to automate repetitive manual work.
- Pipeline reliability instincts. You debug data issues to their root cause, and you think about freshness, volume, duplicates, and late-arriving data before they become incidents.
- Business translation. You translate business needs into data requirements, work with non-technical stakeholders (including Finance), and push back constructively when a request is better solved a different way.
- Multi-repo comfort. Our data platform spans several repositories (transformation, data infrastructure, orchestration, custom connectors, foundational cloud resources). You're comfortable navigating across these boundaries rather than treating dbt as the whole world.
- AI-assisted development. Proven track record of using AI coding tools in your daily workflow.
- Remote ownership. Independence in a remote, global team, with clear written communication and the discipline to document your work.
- Fluent English. Both written and spoken; knowledge of any other language is an advantage.
- Our stack. Hands-on experience with Google BigQuery, Fivetran, and Apache Airflow (Cloud Composer); or an AWS/Azure equivalent.
- Data catalog and governance tooling. Experience with a data catalog (for example OpenMetadata) or data observability tooling (for example Elementary).
- Git-based CI/CD for data. Experience with CI/CD workflows for data projects — branch-based deployments, slim CI, Docker image builds, and automated plan/apply cycles.
- Terraform for data infrastructure. You've provisioned BigQuery datasets, IAM bindings, or GCS buckets with Terraform.
- Python for data integration. You've written Python that talks to REST APIs — pagination, rate limiting, error handling.
- dbt build lifecycle fluency. You understand how dbt compiles Jinja into SQL, how run/test/snapshot phases chain together, and you've written macros beyond basic ref() and source().
- BigQuery performance tuning. Experience with partitioning, clustering, incremental strategies, and reasoning about slot consumption and query costs for large datasets.
- Docker and Kubernetes. Our dbt builds run in containers on GKE. Experience with Docker images and basic Kubernetes concepts (pods, logs, deployments) helps when debugging pipeline issues.
- Pre-commit hooks and code quality automation. Experience with SQLFluff, pre-commit frameworks, or custom validation scripts.
- SaaS source system familiarity. Prior experience modeling data from CRM, support, project management, marketing automation, or ERP platforms.
Be your truest self. Work on your terms. Make a difference.
- Unlimited Vacation
- Flexible Working Options
- Health Insurance
- Parental Leave
- Employee Stock Option Plan
- Home Office Allowance
- Professional Development Stipend
- Peer Recognition Program
DoiT unites as Many Do’ers, One Team, where diversity is more than a goal - it’s our strength. We actively cultivate an inclusive, equitable workplace, recognizing that each unique perspective enhances our innovation. By celebrating differences, we create an environment where every individual feels valued, contributing to our collective success.
#LI-Remote
Benefits
- health insurance
- stock options
- parental leave
- professional development
About this role
DoiT's Analytics Engineering role sits within a larger Business Systems Engineering group responsible for the internal data platform that hundreds of employees rely on monthly. You'd own meaningful portions of the platform's day-to-day engineering—model changes, new data source onboarding, CI/CD, and infrastructure operations—working across 15+ business domains including Finance, Sales, and Product. This isn't a reporting-focused position; it's a full engineering role where you'd treat data models as production code, implement governance and access controls through infrastructure-as-code, build custom Python connectors when needed, and strengthen platform reliability through testing and observability.
The ideal candidate brings 3–5 years of analytics or data engineering experience with strong SQL, dbt proficiency, and software engineering discipline around version control and testing. You should be comfortable navigating multiple repositories, debugging data issues to root cause, and translating business requirements into sound data architecture. The role expects fluency in English, remote self-direction, and active use of AI coding tools to accelerate routine work rather than replace it.
Based remotely in Mexico, Colombia, or other LATAM locations with meaningful US time zone overlap, you'd report to the Head of Business Intelligence and work closely with BI analysts and cross-functional partners. This is a high-ownership position with real room to grow within a company that serves over 4,000 customers across AWS, Google Cloud, and Azure environments.
Typical pay for this role
$140,000
Typical range: $100,000 to $140,000
Pay for this role
The employer didn't post a pay range for this role. That usually means pay is set in negotiation, which favors whoever arrives with numbers. Check ranges on comparable Analytics Engineer postings in your area, and analyze any offer before you accept it.
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