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Senior Data Engineer - d6d
- Insight Global (Miami, FL)
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Job Description
We’re hiring a Senior Data Engineer to help own and evolve our data warehouse and data products end-to-end, partnering closely with Analytics, Operations, and Product/Engineering. This role is ideal for someone with strong data engineering fundamentals who is excited to deepen their skills in dimensional modeling and dbt, and who has interest or experience in machine learning and data science.
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 [email protected] 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/.
Skills and Requirements
• 4+ years in data engineering, analytics engineering, backend engineering with significant data ownership, or similar.
• Strong SQL and experience working in a cloud data warehouse (Snowflake preferred).
• Experience building production pipelines and owning reliability: monitoring, backfills, incident response, and stakeholder communication.
Solid data modeling instincts and willingness to learn or deepen formal dimensional modeling patterns. · dbt experience (models, tests, docs, macros, snapshots) or comparable transformation frameworks.
• Dimensional modeling experience (Kimball concepts like grain, conformed dimensions, SCD1/2, surrogate keys).
• Experience enabling data science or ML workflows: feature datasets, training data generation, data leakage awareness, reproducibility.
Extra Plusses:
• Python for data tooling and automation.
• AWS experience (S3, IAM, Glue/Spark, Lambda) and Terraform.
• Streaming or CDC familiarity.
Data observability tools (Monte Carlo, Great Expectations) and data contracts or semantic layers.
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