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# Data Engineer

- **Company:** Intellisoft Inc
- **Location:** Parsippany-Troy Hills, NJ, United States
- **Experience:** Expert
- **Contract:** Temporary contract
- **Skills:** Amazon Web Services, Data Analysis, Microsoft Azure, Big Data, Continuous Integration, Information Engineering, Data Infrastructure, Extract Transform Load (ETL), Python (Programming Language), NumPy, Standard Sql, Data Streaming, Management of Software Versions, Feature Engineering, GitHub Copilot, Pandas, Data Lakes, Pyspark, Deployment Automation, Bitbucket, Jenkins, Databricks
- **Published:** September 10, 2026
- **Apply:** https://www.dice.com/job-detail/78b4e119-d038-480d-ae2e-21a3cf36bfd2

## About the Role

* Top Must-Haves:
 * **-30% cleaning and handle, 30% building the pipeline, 30 % analytical skills
 * 7+ years (around that) of experience in Data Engineering or Data Platform development.
 * Finance or payroll domain experience is ideal- understands payroll data structures, pay period logic, compensation/deduction relationships, or has worked in financial services data environments.
 * Experience handling large-scale datasets (billions of records, multi-TB, time-series data).
 * Aws cloud experience (or azure cloud if databricks exp is there)
 * Strong Databricks proficiency with deep understanding of:
 * Unity Catalog (governance, access patterns, catalog/schema design)
 * Delta Lake internals (optimization, clustering, change data feed, versioning)
 * Databricks Workflows (orchestration, dependencies, monitoring)
 * Databricks Asset Bundles (Declarative automation bundles) or equivalent deployment frameworks
 * Exposure to architecture-level design decisions (can participate in architecture discussions, understands tradeoffs).
 * Strong proficiency in:

 * Python
 * SQL
 * PySpark
 * Data Modeling
 * ETL/ELT Development

Analytical fluency: exploratory data analysis, basic statistical concepts (correlation, distributions, time-series patterns), feature engineering support.

Proficiency with pandas/numpy for ad-hoc analytical work alongside production PySpark.

CI/CD implementation experience (Bitbucket Pipelines, Jenkins, automated deployment).

Proficiency with AI-assisted development tools (GitHub Copilot, Amazon Q, Kiro, or equivalent).

Data quality and validation frameworks



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