Data Engineer, Solutions & Data — Azure Pipelines

DLA Piper; Minneapolis, Saint Paul (MN, MN); Hybrid; USD 101,000 - 160,000; Full time

Job summary

DLA Piper is seeking a Data Engineer to design, build, and operate data pipelines and integrations for analytics, reporting, and downstream solutions. This role emphasizes governance, data quality, and secure access to integrated data in a hybrid work setting across U.S.

offices. Responsibilities include consolidating data from multiple sources, implementing Azure-based data platforms, and collaborating with cross-functional teams in an agile pod model.

Qualifications

• 3 years of experience in data engineering and/or data platform engineering.
• High School or GED minimum; Bachelor's degree preferred.
• Proficiency in SQL and Python; experience with data pipelines and cloud data services.

Responsibilities

• Build and operationalize data pipelines across heterogeneous environments, aligning to governance principles and SLAs.
• Design and implement data pipelines using Azure data technologies to ingest, process, and deliver data.
• Consolidate data from multiple sources into a centralized integration point and manage field mappings and transformations.
• Identify, troubleshoot, and resolve data quality, integrity, latency, and security concerns.

Skills

SQL

Python

DataOps

PowerShell

Java

Scala

Go

Cross-functional teamwork

Azure Data Factory

Azure Databricks

Azure Event Hubs

SSIS

Azure Synapse Analytics

Education

High School or GED

Bachelor's Degree in Computer Science, Engineering, or related field

Tools

Azure Data Factory

Azure Databricks

Azure Event Hubs

SSIS

Azure Synapse Analytics

SQL Server

Job description

DLA Piper is seeking a Data Engineer to design, build, and operate data pipelines and integrations for analytics, reporting, and downstream solutions. This role emphasizes governance, data quality, and secure access to integrated data in a hybrid work setting across U.S.

offices. Responsibilities include consolidating data from multiple sources, implementing Azure-based data platforms, and collaborating with cross-functional teams in an agile pod model.