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Data Engineer

TechniPros, LLC
New Haven, CT, United States
2 months ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Job source
dice.com

Tech stack

Business Analytics Applications Microsoft Azure Continuous Integration Information Engineering Data Integration Extract Transform Load (ETL) Data Warehousing Python (Programming Language) Power BI Azure Data Lake SQL Databases Data Processing
+8 more
Azure Data Factory Git Microsoft Fabric Pyspark Data Management Azure Synapse Analytics Data Pipelines Databricks

Job description

We are seeking an experienced Data Engineer with strong expertise in Azure Data Services and Microsoft Fabric to design, develop, and optimize enterprise-scale data platforms. The ideal candidate will lead data engineering initiatives, build scalable data pipelines, implement modern data architectures, and collaborate with business and technical stakeholders to deliver high-quality analytics solutions., * Design and develop scalable data pipelines and ETL/ELT solutions.

  • Build and maintain data lake, lake house, and data warehouse solutions.
  • Work with Microsoft Fabric and Azure services for data integration and analytics.
  • Optimize data processing, performance, and storage.
  • Collaborate with business stakeholders, analysts, and developers.
  • Ensure data quality, security, and governance standards.
  • Lead technical discussions and mentor junior team members.

Requirements

  • Microsoft Fabric (Lakehouse, One Lake, Data Factory)
  • Azure Data Factory (ADF)
  • Azure Synapse Analytics
  • Azure Data Lake Storage (ADLS Gen2)
  • Azure Databricks
  • Python, PySpark
  • SQL/T-SQL
  • Data Warehousing & Data Modeling
  • Power BI
  • Azure DevOps, Git, CI/CD

Apply for this position

This job is hosted externally. Click below to view the full posting and apply.

Apply on dice.com

Good distractions

Talks and stories from around this role — technically off-topic, practically not.

Moments

Explore playlists

6:21 min

Investigating push inefficiencies with upstream Git experts

Jonathan Creamer · Coffee With Developers

4:32 min

Harnessing Spark with Python using PySpark and Py4J

Ayon Roy · LIVE

1:24 min

Moving the semantic layer upstream to avoid vendor lock-in

Piotr Menclewicz Piotr Menclewicz · Europe 2026 Virtual

2:57 min

Core technical practices for robust data engineering

Sandhya Menon Sandhya Menon · WWC Europe 2026

1:46 min

Traditional data architecture before Microsoft Fabric

Dr. Alexander Wachtel Dr. Alexander Wachtel +1 · WWC 2025

3:37 min

Scaling machine learning pipelines from prototypes to petabytes

Julian Joseph · LIVE

Videos

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Enjoying SQL data pipelines with dbt thumbnail
26:53
WWC 2023 • almost 3 years ago

Enjoying SQL data pipelines with dbt

Matthias Niehoff

PySpark - Combining Machine Learning & Big Data thumbnail
43:26
LIVE • almost 6 years ago

PySpark - Combining Machine Learning & Big Data

Ayon Roy

How a Small Team Shrank a Microsoft Monorepo by 94% thumbnail
33:23
Coffee With Developers • almost 2 years ago

How a Small Team Shrank a Microsoft Monorepo by 94%

Jonathan Creamer

Beyond Dashboards: Fixing Text-to-SQL with Semantic RAG thumbnail
7:02
Europe 2026 Virtual • about 1 month ago

Beyond Dashboards: Fixing Text-to-SQL with Semantic RAG

Piotr Menclewicz

Data Analytics with Microsoft Fabric: End-to-End Use Case with Data Agents thumbnail
26:58
WWC 2025 • 12 months ago

Data Analytics with Microsoft Fabric: End-to-End Use Case with Data Agents

Dr. Alexander Wachtel, Hanna Schwab

Data Science in Retail thumbnail
58:21
LIVE • about 3 years ago

Data Science in Retail

Julian Joseph

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