INTL LATAM Data Engineer

Insight Global
United States
1 day ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Experienced
Experience required
2 years minimum
Working hours
Regular working hours
Job source

Tech stack

Query Performance Airflow Automation of Tests Microsoft Azure Big Data BigQuery Code Review Continuous Integration Information Engineering Data Governance Data Infrastructure Dimensional Modeling
+24 more
Github Apache Hive Python (Programming Language) PostgreSQL Query Optimization Standard Sql SQL Databases Datadog Circleci Snowflake Indexer Git Containerization Data Lakes Gitlab-ci Information Technology Bitbucket Data Management Cerner EMPI Terraform Data Pipelines User Identification Docker Databricks

Job description

We are looking for a Data Engineer to build and operate the data platform that Herself Health runs on. Reporting to the Director of Information Technology, you will own dbt models end to end - from source ingestion through the marts that power Databricks BI dashboards, payor reporting, quality measures, and risk adjustment analytics.

This role is hands-on and delivery-focused. You should already be productive in dbt and comfortable working the way a software team works: feature branch, pull request, automated tests and CI checks, review, then a controlled promotion through development, staging, and production. We are looking for someone who treats data pipelines as production software - versioned, tested, reviewed, observable, and reversible.

Our stack is Databricks (Unity Catalog, Delta), dbt for transformation, Prefect for orchestration, Bitbucket based CI/CD (CircleCI), and databricks and a custom reporting solution as the consumption layer. You will work alongside analysts who depend on your marts being correct, and clinical and finance stakeholders whose revenue and quality reporting depend on them being on time.

We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to HR@insightglobal.com.To learn more about how we collect, keep, and process your private information, please review Insight Global’s Workforce Privacy Policy: https://insightglobal.com/workforce-privacy-policy/.

Requirements

· 3-6 years of professional data engineering or analytics engineering experience, with at least 2 years building and owning dbt projects in production

· Strong SQL, including window functions, complex joins, set operations, and query performance tuning on large datasets

· Demonstrated command of dbt beyond the basics: incremental strategies, materializations, snapshots, macros, packages, tests, and project structure and naming conventions

· Practical experience with Git and a pull-request-based CI/CD process, and a clear understanding of promoting data changes from development through staging to production

· Experience with a CI platform such as CircleCI, GitHub Actions, GitLab CI, or Azure DevOps, including configuring automated checks for a dbt project

· Hands-on experience with a cloud lakehouse or warehouse - Databricks strongly preferred (Unity Catalog, Delta Lake, Spark SQL); Snowflake or BigQuery experience translates

· Proficiency in Python for ingestion, orchestration, and tooling

· Experience with a workflow orchestrator (Prefect, Airflow, Dagster, or Databricks Workflows)

· Working knowledge of dimensional modeling and when to depart from it

· Familiarity with data governance, access control, security, and HIPAA-compliant handling of PHI

· Clear written communication - you can explain a modeling decision or an incident to both an engineer and a clinical operations leader

· Comfort working in a fast-paced, ambiguous, and evolving environment with limited process scaffolding · Experience with Postgres as a serving layer, including indexing and query tuning

· Data quality and observability tooling (dbt artifacts-based monitoring, Elementary, Monte Carlo, or similar)

· Infrastructure as code (Terraform) and containerization (Docker)

· Experience with identity resolution or master data management (EMPI or similar)

· Exposure to streaming or near-real-time ingestion patterns

· Experience mentoring analysts or junior engineers on SQL, dbt, and code review

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