Data Operations Engineer, Development (52338)

Citrin Cooperman offers a dynamic work environment, fostering professional growth and collaboration. We’re continuously seeking talented individuals who bring a problem-solving mindset, fresh perspectives, and sharp technical expertise. We know you have choices, so our team of collaborative, innovative professionals are ready to support your professional development. At Citrin Cooperman, we offer competitive compensation and benefits and most importantly, the flexibility to manage your personal and professional life to focus on what matters most to you!

We are seeking an Data Engineer, Development, to join our Development team within the Information Technology department. They’ll help design, construct, maintain, and optimize the core data pipelines that power our business intelligence, analytics, and downstream applications.

In this role, you’ll be a vital part of our operational “Base Plan”, focusing on the rigorous, traditional data engineering required to run a data-driven enterprise. You’ll work within the Microsoft Fabric ecosystem (OneLake, Notebooks, Data Factory) to move data from legacy on-premises systems and third-party APIs into clean, modeled, and highly available data products. Working closely with our Senior Data Engineers and Database Administrator, you’ll take ownership of daily pipeline health, troubleshoot failures, implement data quality checks, and exercise independent judgment in designing solutions and optimizing data workflows. The ideal candidate is a detail-oriented builder who takes pride in writing clean code, designing elegant data models, and ensuring the operational stability of mission-critical data infrastructure.

Responsibilities are, but not limited to:

• Pipeline Development: Design, build, and deploy robust ETL/ELT pipelines using Fabric Data Factory and Spark (PySpark/SQL) to ingest data from diverse sources into OneLake, determining appropriate technical approaches and design patterns.
• Data Modeling & Architecture: Implement and maintain the “Medallion Architecture” (Bronze, Silver, Gold layers). Transform raw data into structured, business-ready models (e.g., Star Schemas) optimized for Power BI and standard analytics, including recommending and refining data architecture standards.
• Operational Monitoring & Support: Take ownership of daily pipeline execution. Monitor system health, troubleshoot job failures, debug data anomalies, and ensure Service Level Agreements (SLAs) for data delivery are consistently met, applying discretion to resolve complex data and system issues.
• Data Quality & Governance: Develop and automate data quality validations and anomaly detection scripts to ensure stakeholders are always querying accurate, trustworthy information, designing solutions to proactively improve data integrity.
• Collaboration & Refactoring: Work closely with the Senior DBA to optimize query performance against source systems. Assist in refactoring legacy stored procedures and custom applications into modern, cloud-native Fabric workloads, contributing to technical design decisions and modernization strategies.
• Version Control & CI/CD: Write clean, modular, and well-documented code. Participate in code reviews and utilize Git for version control and automated deployment of data artifacts, following and helping to enhance engineering best practices.

The ideal candidate must:

• Have a bachelor’s degree in computer science, data engineering, mathematics, or equivalent practical experience.
• Be Microsoft Certified: Fabric Data Engineer Associate (DP-700).
• Be Microsoft Certified: Fabric Analytics Engineer Associate (DP-600).
• Be Microsoft Certified: Power BI Data Analyst Associate (PL-300).
• Have 3-5+ years of professional experience in data engineering, BI development, or data warehousing.
• Be strongly proficient in SQL and Python (specifically PySpark) for data transformation and analysis.
• Have hands-on experience with modern cloud data platforms, preferably Microsoft Fabric, Azure Synapse Analytics, Azure Data Factory, or Databricks.
• Have a solid understanding of relational database concepts, data warehousing principles (Kimball methodology), and modern data lake house architecture.
• Have experience with operational monitoring, alerting, and incident response for data pipelines including diagnosing and resolving complex technical issues.
• Be familiar with source control (Git) and CI/CD principles and implementation for data deployments.
• Be reliability-focused: Deeply values stability and predictability. Understands that a boring, perfectly executing pipeline is the goal of data operations.
• Be detail-oriented: Sweats the small stuff when it comes to data types, schema design, and naming conventions, knowing that messy data at the foundation causes massive issues downstream.
• Be a collaborative team player: Eager to learn from senior architects and DBAs, and equally willing to help business analysts understand the underlying data models.