Independent Contractor, Data Engineer, Business Operations
Description
Independent Contractor, Data Engineer, Business Operations
What You’ll Do
We’re seeking a hands-on Data Engineer to partner with Business Operations in building a reliable, scalable data foundation. You’ll centralize operational data across core systems, develop automated pipelines and models that power self-serve analytics, and replace manual reporting with scheduled, high-trust data products. You’ll also lay the groundwork for advanced use cases such as forecasting, decision support, and natural-language access to trusted data—accelerating better, faster decisions across the organization.
A Day in the Life
Deliverables
- Stand up a scalable Databricks lakehouse to ingest, model, and serve business operations data (finance, resourcing, project delivery, CRM, marketing, and time tracking).
- Design and maintain automated ELT/ETL pipelines that move data from SaaS tools, databases, and files into bronze/silver/gold layers.
- Build the core semantic layer (cleaned, conformed, documented tables) that powers self-serve BI and executive dashboards.
- Replace legacy/manual engagement and utilization reports with scheduled, monitored jobs and SLAs.
- Partner with Business Operations, Finance, and People Operations leaders to define source-of-truth metrics (e.g., revenue, margin, utilization, velocity, pipeline, engagement health).
- Lay groundwork for AI use cases (RAG over operational data, agentic processes, querying company data) by implementing robust lineage, metadata, and access controls.
- Architecture & Modeling: Design lakehouse architecture, dimensional/medallion models, and data contracts across systems.
- Pipeline Automation: Implement CI/CD for data (branching, PRs, jobs, environments), with observability and reproducibility.
- Data Governance: Enforce PII/PHI handling, role-based access, auditability, and retention aligned to healthcare-adjacent standards.
- Enablement: Document datasets, publish a data catalog, and enable self-serve usage via BI and SQL.
- Reporting Modernization: Decommission manual spreadsheets and one-off extracts; consolidate to certified, scheduled outputs.
- AI Readiness: Capture lineage/metadata and vector-friendly document stores to support future ML and RAG initiatives.
What You Must Have
Education and Experience
- 2+ years in data engineering or analytics engineering, including building production data pipelines at scale.
- Expert with Databricks (Delta Lake, SQL, PySpark) and cloud data platforms (AWS or Azure).
Skills and Competencies
- Proficient with dbt and/or Delta Live Tables; strong SQL and data modeling fundamentals.
- Experience orchestrating jobs (Airflow, Databricks Workflows, or equivalent)
- Comfortable with PowerBI and semantic modeling for self-serve analytics.
- Familiarity with data governance (RBAC/ABAC, secrets management, token-based auth) and healthcare-adjacent compliance (e.g., HIPAA concepts) is a plus.
- Strong stakeholder skills. Can translate business needs into reliable data products and clear SLAs.
- Databricks, Delta Lake, PySpark, SQL, dbt, REST/GraphQL APIs, Git/GitHub, Power BI/Tableau/Looker.