DoiT - Analytics Engineer
Requirements
• 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). • Strong SQL and data modeling. • 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. • dbt / Data transformation proficiency. • 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. • Software engineering discipline. • 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. • Pipeline reliability instincts. • 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. • Business translation. • 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. • Multi-repo comfort. • AI-assisted development. Proven track record of using AI coding tools in your daily workflow. • AI-assisted development. • Remote ownership. Independence in a remote, global team, with clear written communication and the discipline to document your work. • Remote ownership. • Fluent English. Both written and spoken; knowledge of any other language is an advantage. • Fluent English. • Our stack. Hands-on experience with Google BigQuery, Fivetran, and Apache Airflow (Cloud Composer); or an AWS/Azure equivalent. • Our stack. • Data catalog and governance tooling. Experience with a data catalog (for example OpenMetadata) or data observability tooling (for example Elementary). • Data catalog and governance tooling. • 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. • Git-based CI/CD for data. • Terraform for data infrastructure. You've provisioned BigQuery datasets, IAM bindings, or GCS buckets with Terraform. • Terraform for data infrastructure. • Python for data integration. You've written Python that talks to REST APIs — pagination, rate limiting, error handling. • Python for data integration. • 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(). • dbt build lifecycle fluency. • BigQuery performance tuning. Experience with partitioning, clustering, incremental strategies, and reasoning about slot consumption and query costs for large datasets. • BigQuery performance tuning. • 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. • Docker and Kubernetes. • Pre-commit hooks and code quality automation. Experience with SQLFluff, pre-commit frameworks, or custom validation scripts. • Pre-commit hooks and code quality automation. • SaaS source system familiarity. Prior experience modeling data from CRM, support, project management, marketing automation, or ERP platforms. • SaaS source system familiarity. • Are you a Do’er?Be your truest self. Work on your terms. Make a difference. • Are you a Do’er? • 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.
Responsibilities
• 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. • Build, test, and maintain data models and pipelines • 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. • Manage data infrastructure as code. • 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. • Own data access and governance. • Improve platform reliability by strengthening testing standards, observability, and alerting, so issues are caught early rather than reported by users. • Improve platform reliability • 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. • Support orchestration and scheduling. • 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. • Build and maintain custom data connectors. • 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. • Partner with BI analysts • Use modern AI tooling to draft code and accelerate routine work, amplifying your output rather than adding manual effort. • Use modern AI tooling
Benefits
• Unlimited Vacation • Flexible Working Options • Health Insurance • Employee Stock Option Plan • 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. • Many Do’ers, One Team
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