Data Engineer

Lowenstein Sandler•Roseland, NJ

1d•$125,000 - $140,000•Hybrid

About The Position

The Data Engineer is a central member of the Software Engineering team, responsible for building and maintaining data pipelines that feed Lowenstein Sandler’s Microsoft Fabric Data Lakehouse. Working with the Data Architect, the engineer ingests, transforms, and curates data from the firm’s core business systems through a medallion architecture to provide trusted, analytics-ready datasets for reporting, business intelligence, and AI-driven applications. The position calls for an engineer who can work in a fast-paced environment, maintain rigorous data quality and governance, and incorporate AI tooling into the development workflow. The role partners with the Data Architect and stakeholders across Finance, Marketing, Human Resources, practice groups, and IT to implement governed data models, provide dependable datasets, and support business intelligence developers and self-service users. It also includes maintaining documentation and operational runbooks, managing deployment of data artifacts, and providing production support and escalation-level troubleshooting. The position is based in Roseland, New Jersey, in a hybrid work arrangement. It is full-time, Monday through Friday, 9:00 AM–5:00 PM, with no travel required. The expected salary range is $125,000–$140,000, with exact compensation determined by candidate qualifications and location. Benefits include medical, dental, vision, prescription drug, life insurance, disability, FSAs, 401(k), and paid time off.

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 proven hands-on experience with Microsoft SQL Server, 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 loading, 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 Git source control 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.
Exceptional communication skills, including the ability to translate technical concepts for non-technical stakeholders.
Ability to work independently and collaboratively in a team-oriented environment.
Continuous learning and active 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.
SALI/LMSS taxonomy experience.
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 bronze, silver, and gold layers of the medallion architecture, covering raw ingestion, cleansing and conformance, and curated analytics-ready models.
Partner 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, incorporating data-quality validation, source reconciliation, exception handling, and alerting.
Trace data discrepancies from source systems through bronze, silver, and gold layers to the final DAX measure or report, and identify and resolve 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; provide production support and escalation-level troubleshooting for failed or degraded data loads.