Data Engineer

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Company Overview

A leading global law firm provides legal services and business solutions to clients across a wide range of industries. The organization fosters a bold, collaborative, exceptional, and supportive work environment where professionals can engage in meaningful work, develop their expertise, and grow their careers.

Key Responsibilities

Data Pipeline Engineering & Integration

• Build and operationalize data pipelines across heterogeneous environments while aligning with governance principles and service-level expectations.
• Build and maintain ingestion, transformation, and publication pipelines to deliver analytics-ready data.
• Consolidate data from multiple sources into centralized integration points, such as SQL Server.
• Manage field mappings and transformations to support consistent downstream data consumption.
• Develop reliable and reusable data integration processes that support business and technology initiatives.

Data Quality, Reliability & Operations

• Identify, troubleshoot, and resolve data quality, integrity, latency, and security issues.
• Apply monitoring and operational best practices to maintain reliable and performant pipelines.
• Contribute to data quality and governance practices, including dataset profiling and defining data quality rules.
• Establish monitoring and remediation processes to improve pipeline reliability and operational readiness.
• Support automation initiatives that reduce manual effort and improve data engineering efficiency.

Collaboration & Delivery

• Work cross-functionally with engineers, analysts, and business stakeholders to understand requirements and deliver effective data solutions.
• Participate in sprint-based delivery within an Agile pod model.
• Produce reusable data assets and integration components that can be leveraged across multiple initiatives.
• Collaborate with data engineering and other technical teams to improve data integration, governance, and operational processes.
• Support consistent analytics and AI consumption through standardized data integrations and transformations.

Required Qualifications

• Minimum of 3 years of experience in data engineering and/or data platform engineering, including pipelines, integration, and operational support.
• Proficiency in SQL and Python.
• Experience with data pipeline tooling and cloud data services, particularly Azure Data Factory, Azure Databricks, Azure Event Hubs, and SSIS.
• Experience with data warehousing technologies, particularly Azure Synapse Analytics.
• Strong understanding of data modeling, data warehousing, and data governance.
• Scripting and automation skills using PowerShell or related technologies.
• Experience integrating data from multiple enterprise source systems into centralized SQL-based integration environments.
• Familiarity with DataOps concepts and cross-functional data engineering environments.
• Strong troubleshooting, analytical, communication, and collaboration skills.

Preferred Qualifications

• Familiarity with additional programming languages such as Java, Scala, or Go.
• Experience working with enterprise-scale data integration environments.
• Experience developing reusable data integrations and standardized transformations.
• Knowledge of data quality monitoring, governance, and operational data platform practices.
• Experience supporting analytics and AI data consumption.
• Ability to work effectively within cross-functional Agile teams.

Education Requirements

Minimum Education: High School diploma or GED.

Preferred Education: Bachelor’s Degree in Computer Science, Engineering, or a related field.

Success in This Role

Success in this position will include delivering reliable data pipelines that provide trusted, high-quality data within agreed service levels, enabling faster onboarding of new data sources, supporting consistent analytics and AI consumption, reducing manual effort through reusable integrations and standardized transformations, and improving overall data reliability and operational readiness.

Disclaimer

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