Data Engineer

Lowenstein Sandler•Roseland, NJ

9d•$125,000 - $140,000•Hybrid

About The Position

The Data Engineer is a central member of the Software Engineering team, building and maintaining data pipelines that feed Lowenstein Sandler’s Microsoft Fabric Data Lakehouse. In collaboration with the Data Architect, the role ingests, transforms, and curates data from core business systems through a medallion architecture, producing trusted, analytics-ready datasets for reporting, business intelligence, and AI-driven applications across the firm. The position calls for a fast-paced, quality- and governance-focused engineer who incorporates AI tooling into their development workflow. The role partners with the Data Architect and stakeholders across Finance, Marketing, Human Resources, practice groups, and IT. It supports business intelligence developers and self-service users, maintains data documentation and operational runbooks, and provides production support and escalation-level troubleshooting. The position is based in Roseland, New Jersey, on a hybrid schedule, Monday through Friday, 9:00 AM to 5:00 PM, with no travel required.

Requirements

3–5+ years of hands-on ETL/ELT or data engineering experience building and supporting production data pipelines.
Working knowledge of medallion architecture, lakehouse and data warehouse concepts, and dimensional modeling.
Strong T-SQL skills and hands-on MS SQL Server experience, including query optimization and performance tuning.
Proficiency in Python and PySpark, or demonstrated ability to ramp up quickly on Spark-based transformation.
Experience with incremental loads, change data capture, and slowly changing dimension patterns.
Experience ingesting data from REST APIs, JSON and XML payloads, flat files, SFTP sources, and direct database connections.
A rigorous approach to data quality, including validation, reconciliation, and alerting.
Familiarity with source control (Git) and CI/CD practices for data artifacts.
Experience building, maintaining, and supporting analytics models for reporting.
Ability to deliver high-quality work quickly in a fast-paced, deadline-driven environment and manage competing priorities effectively.
Willingness to adopt AI-assisted development tooling as part of a regular engineering workflow.
Bachelor’s degree in Computer Science, Information Systems, Data Analytics, or a related field, or equivalent practical experience of 5+ years in data engineering.
Strong analytical and problem-solving skills and excellent attention to detail.
Excellent communication skills, including the ability to explain technical concepts to non-technical stakeholders.
Ability to work independently and collaboratively in a team-oriented environment.
Commitment to continuous learning and experimentation with emerging tools, especially in data and AI.

Nice To Haves

Microsoft Fabric experience; equivalent experience with Azure Data Factory, Synapse Analytics, Databricks, SSIS, dbt, or other modern ETL frameworks will also be considered.
Power BI development experience, including semantic models, DAX, and report delivery.
KQL experience.
Experience with Claude Code, GitHub Copilot, or equivalent AI-assisted tooling.
Experience with the SALI/LMSS legal data taxonomy.
Law firm data experience, including Aderant, Intapp, iManage, InterAction, or Dayforce.

Responsibilities

Design, build, test, and maintain ETL/ELT pipelines that ingest enterprise-system data into the Microsoft Fabric Data Lakehouse using Data Factory pipelines and Spark notebooks.
Implement and maintain the bronze, silver, and gold layers of the medallion architecture for raw ingestion, cleansing and conformance, and curated analytics-ready models.
Work with the Data Architect to translate logical data models into physical implementations, including star schemas, slowly changing dimensions, and Power BI semantic models in the gold layer.
Build and maintain integrations with Aderant Expert, Intapp, iManage, InterAction, Dayforce, SharePoint Online, and third-party APIs; apply incremental-load and change-data-capture patterns and normalize key entities to the SALI/LMSS taxonomy.
Develop transformations in T-SQL, Spark SQL, and PySpark, including data-quality validation, reconciliation to source, exception handling, and alerting.
Trace data discrepancies from source systems through bronze, silver, and gold layers to final DAX measures or reports, identifying and resolving root causes.
Monitor, tune, and optimize pipeline performance, refresh windows, and Fabric capacity consumption to control costs and meet business service levels.
Implement data governance and security controls with the Data Architect and Information Security, including workspace permissions, row-level security, sensitivity labeling, and law firm confidentiality and ethical-wall requirements.
Gather requirements with Finance, Marketing, Human Resources, practice groups, and IT stakeholders; support business intelligence developers and self-service users with reliable, well-modeled, documented datasets.
Manage data artifacts through source control and deployment pipelines, using Fabric Git integration and Azure DevOps to promote changes across development, test, and production workspaces.
Use Claude Code and other AI-powered development tools to accelerate pipeline development, testing, and documentation, and build curated datasets for the firm’s AI and agentic applications.
Maintain data dictionaries, lineage, and operational runbooks, and provide production support and escalation-level troubleshooting for failed or degraded data loads.