DoiT International

Cloud consulting and optimization platform

Analytics Engineer

Data EngineeringPosted on Nov 10

Full-timeNo salary listed

About the Job

Requirements

Three to five years of experience in analytics engineering, data engineering, or BI engineering, with a track record of delivering data models that others depend on.
Strong SQL skills, including writing, reading, and optimizing complex analytical queries; reasoning about grain and joins; and designing layered models with sound judgment about where logic belongs.
Professional experience building and maintaining dbt projects, including packages, macros, incremental strategies, snapshots, seeds, and CI/CD; implementing staging, intermediate, and mart patterns; and understanding deferred builds, state comparison, and slim CI.
Software engineering discipline with version control, code review, automated testing, and CI/CD, plus a drive to automate repetitive manual work.
Ability to debug data issues to root cause and consider freshness, volume, duplicates, and late-arriving data before they become incidents.
Ability to translate business needs into data requirements, work with non-technical stakeholders including Finance, and constructively challenge requests that would be better solved another way.
Comfort navigating multiple repositories across transformation, data infrastructure, orchestration, custom connectors, and foundational cloud resources.
Proven track record of using AI coding tools in a daily workflow.
Ability to work independently in a remote, global team, communicate clearly in writing, and document work.
Fluency in spoken and written English.

Responsibilities

Share ownership of platform engineering, including routine model changes, the engineering backlog, onboarding new data sources from raw ingestion to certified datasets, maintaining CI/CD, and operating infrastructure.
Build, test, and maintain data models and pipelines for dashboards, finance and investor reporting, and the AI analytics layer across more than 15 business domains, including Finance, Sales, Customer Experience, Marketing, Product, and HR.
Manage data infrastructure as code by provisioning and configuring BigQuery datasets, views, GCS buckets, and IAM permissions through Terraform; onboard ingestion destinations, manage access controls, and handle schema drift on source tables.
Own and expand the role-based access control implementation as domains and consumers are added; keep data catalog metadata such as tier, domain, owner, and PII tags current; and maintain documentation.
Improve platform reliability by strengthening testing standards, observability, and alerting so issues are caught early.
Support orchestration and scheduling by understanding how Airflow DAGs run dbt builds on daily, hourly, and monthly schedules, tagging models for the appropriate schedule, and troubleshooting build failures in Cloud Composer.
Build and maintain custom data connectors by writing and maintaining Python-based Fivetran SDK connectors when native connectors do not cover a SaaS source, including pagination, rate limiting, error handling, and automated tests.
Partner with BI analysts to turn ambiguous questions into well-modeled, trustworthy datasets, and improve team engineering practices through code review and shared standards.
Use modern AI tooling to draft code and accelerate routine work.

Desired Qualifications

Hands-on experience with Google BigQuery, Fivetran, and Apache Airflow (Cloud Composer), or equivalent AWS or Azure services.
Experience with a data catalog such as OpenMetadata or data observability tooling such as Elementary.
Experience with Git-based CI/CD workflows for data projects, including branch-based deployments, slim CI, Docker image builds, and automated plan/apply cycles.
Experience provisioning BigQuery datasets, IAM bindings, or GCS buckets with Terraform.
Experience writing Python integrations with REST APIs, including pagination, rate limiting, and error handling.
Understanding of dbt's build lifecycle, including Jinja compilation into SQL, run/test/snapshot phases, and writing macros beyond basic ref() and source().
Experience tuning BigQuery performance with partitioning, clustering, incremental strategies, and reasoning about slot consumption and query costs for large datasets.
Experience with Docker images and basic Kubernetes concepts such as pods, logs, and deployments.
Experience with SQLFluff, pre-commit frameworks, or custom validation scripts.
Prior experience modeling data from CRM, support, project management, marketing automation, or ERP platforms.
Knowledge of additional languages is an advantage.

LocationAs 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 AreDoiT 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.
Use modern AI tooling to draft code and accelerate routine work, amplifying your output rather than adding manual effort.

Qualifications

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.

Bonus Points

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.

Are you a Do’er?Be your truest self. Work on your terms. Make a difference.

We are home to a global team of incredible talent who work remotely and have the flexibility to have a schedule that balances your work and home life. We embrace and support leveling up your skills professionally and personally.

What does being a Do’er mean? We’re all about being entrepreneurial, pursuing knowledge, and having fun! Click here to learn more about our core values.

Sounds too good to be true? Check out our Glassdoor Page.

We thought so too, but we’re here and happy we hit that ‘apply’ button.

Full-time employee benefits include:

Unlimited Vacation
Flexible Working Options
Health Insurance
Parental Leave
Employee Stock Option Plan
Home Office Allowance
Professional Development Stipend
Peer Recognition Program

Many Do’ers, One TeamDoiT 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

About the Company

DoiT International

Cloud consulting and optimization platform

DoiT International provides cloud consulting and technology solutions to help businesses optimize their cloud operations. The company offers expert services alongside proprietary software tools to manage cloud environments, focusing on cloud migration, cost optimization, and performance tuning. It collaborates with major cloud platforms such as Google Cloud and AWS to deliver tailored solutions and subscriptions, generating revenue from consulting fees and software licenses. DoiT differentiates itself through its direct partnerships with large cloud providers, its suite of in-house tools, and a blended business model that combines one-time consulting with recurring software subscriptions. Its goal is to help clients accelerate digital transformation, maximize cloud investments, and maintain efficient, scalable cloud environments for a diverse client base.

Company size: 501-1,000
Company stage: Series A
Total funding: $100M
Headquarters: San Francisco, CA
Founded: 2011

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