Analytics Engineer

- 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.

- 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().

- 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.

- 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.

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

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).

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.