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Added 3 days ago
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Data Engineer
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Persistent Systems

Data Engineer

New York, NY, United States

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Job details

Apply by: Oct 29, 2027
Experience: 3–5 years of professional experience in data engineering, ETL/ELT development, or data warehousing, including building and supporting production data pipelines
Posting language: English
Posted: Oct 29, 2026

Original job description

Job Description & Responsibilities

Persistent Systems is seeking a Data Engineer to join our team in our New York City headquarters. The Data Engineer will design, build, and operate the pipelines, lakehouses, and data models that deliver trusted, well-governed data for analytics, reporting, and AI solutions, with a primary focus on Microsoft Fabric and the Azure data platform.

This role sits between source systems and the people and applications that depend on them: ingesting operational, file, API, and streaming data; transforming it through medallion layers in OneLake; and publishing curated, secure datasets for Power BI, data science, and retrieval-augmented generation (RAG) workloads, while integrating tools from other providers where solutions require them.

The ideal candidate is a pragmatic builder who treats data pipelines as production software. They write clean, testable SQL and PySpark, care about data quality and lineage as much as throughput, and can explain tradeoffs among cost, latency, data freshness, and complexity to both technical and nontechnical stakeholders.

Position Responsibilities

Own data engineering work from source-system discovery and requirements through design, build, testing, deployment, monitoring, and ongoing support
Partner with analysts, BI developers, AI engineers, architects, security, and business stakeholders to define data requirements, freshness targets, and acceptance criteria
Build reusable ingestion patterns, transformation frameworks, and data contracts so that pipelines, definitions, and lineage are consistent and maintainable across teams
Evaluate Microsoft Fabric, Azure, and complementary tools against requirements for scale, performance, security, and total cost of ownership, and track platform changes and new features
Document designs, data flows, and operating procedures clearly, review peers’ work, and contribute to a culture of engineering discipline
Design and implement medallion (bronze, silver, gold) architectures in Microsoft Fabric Lakehouse and Warehouse on OneLake using Delta Lake tables, OneLake shortcuts, and appropriate partitioning and table optimization strategies
Build batch and incremental ingestion with Fabric Data Factory pipelines, Copy job, Dataflow Gen2, and Azure Data Factory, and replicate operational databases into OneLake using mirroring and change data capture (CDC)
Develop transformation logic in Spark notebooks (PySpark and Spark SQL), T-SQL, and KQL; handle duplicate, missing, and late-arriving data; and implement slowly changing dimensions and incremental merge patterns
Build and maintain pipelines in Azure Databricks using Lakeflow Jobs, Lakeflow Spark Declarative Pipelines, Auto Loader, and Unity Catalog, and integrate them with OneLake and Azure Data Lake Storage Gen2
Ingest and process streaming data with Fabric Real-Time Intelligence (Eventstreams, Eventhouse, and Activator), Azure Event Hubs, and Spark Structured Streaming
Design star-schema fact and dimension models and collaborate with BI developers on Power BI semantic models, including Direct Lake, to deliver performant and consistent reporting
Implement validation rules, schema enforcement, reconciliations, and automated data quality tests; monitor pipelines with the Fabric Monitoring hub, Azure Monitor, and Log Analytics; and resolve failures through root-cause analysis
Apply OneLake security, row- and column-level security, dynamic data masking, sensitivity labels, and Microsoft Purview cataloging and lineage; secure access with Microsoft Entra ID, managed identities, and Azure Key Vault
Manage notebooks, pipelines, and SQL database projects in Git-based version control (for example, Azure Repos); promote changes through Fabric deployment pipelines and Azure Pipelines with automated tests and approvals; and provision Azure resources with Bicep or Terraform
Prepare curated datasets and document-processing pipelines that feed Azure AI Search, Microsoft Foundry, and Copilot solutions, including chunking, metadata enrichment, and freshness monitoring for RAG
Tune Spark jobs, Delta tables (OPTIMIZE, V-Order, and liquid clustering or Z-ordering), warehouse queries, and pipeline schedules, and monitor Fabric capacity consumption and Azure spend

Tools and Systems You’ll Use

Microsoft Fabric

OneLake, Lakehouse, Warehouse, Data Factory (pipelines, Copy job, Dataflow Gen2), mirroring, shortcuts, Spark notebooks, Real-Time Intelligence (Eventstream, Eventhouse, Activator), Direct Lake

Azure data services

Azure Data Factory, Azure Databricks (Unity Catalog, Lakeflow), Azure Data Lake Storage Gen2, Azure SQL Database, SQL Server, Azure Event Hubs, Azure Functions, Azure Cosmos DB

Languages and processing

SQL and T-SQL, Python, PySpark, Spark SQL, KQL, Delta Lake, Parquet, JSON, REST APIs, PowerShell

DataOps and automation

Git-based version control (for example, Azure Repos, GitLab, or Bitbucket), Azure Pipelines, Azure Boards, Fabric deployment pipelines, SQL database projects, Bicep, Terraform, Azure CLI, automated data testing

Governance and operations

Microsoft Purview, Microsoft Entra ID, managed identities, Azure Key Vault, OneLake security, Fabric Monitoring hub, Fabric Capacity Metrics app, Azure Monitor, Log Analytics

Valuable adjacent skills

Power BI semantic models, Power Platform (Power Apps, Power Automate, Dataverse), Azure AI Search, Microsoft Foundry, Copilot in Fabric, dbt, Apache Airflow, Apache Kafka, Snowflake, Claude Code or Codex for development

Job Qualifications & Experience

Minimum Qualifications

3–5 years of professional experience in data engineering, ETL/ELT development, or data warehousing, including building and supporting production data pipelines
Bachelor’s degree in computer science, information systems, engineering, mathematics, or a related field, or an equivalent combination of education and relevant professional experience
Advanced SQL proficiency, including complex joins, common table expressions, window functions, MERGE and upsert logic, query tuning, and validation against source systems
Strong Python skills for data engineering, including PySpark or Spark SQL for distributed data processing
Hands-on experience building pipelines with Fabric Data Factory, Azure Data Factory, or Azure Databricks, including parameterization, scheduling, error handling, and incremental loads
Experience designing lakehouse or data warehouse solutions using Delta Lake or comparable table formats, medallion architecture, and dimensional modeling
Working knowledge of Azure storage, identity, networking, and RBAC, including secure data access with Microsoft Entra ID
Experience with Git-based version control and CI/CD practices for data workloads, including branching, code review, and environment promotion
Demonstrated ability to troubleshoot pipeline failures and data quality issues, communicate clearly, and document data flows and lineage

Preferred Qualifications

Production experience with Microsoft Fabric, including OneLake, Lakehouse, Warehouse, mirroring, shortcuts, and Fabric deployment pipelines
Experience with real-time data using Fabric Real-Time Intelligence, Eventstreams, Eventhouse and KQL, Azure Event Hubs, or Spark Structured Streaming
Experience with Azure Databricks, Unity Catalog, and Lakeflow, including integration with Microsoft Fabric
Experience building release pipelines in Azure DevOps (Azure Repos and Azure Pipelines) and provisioning data infrastructure with Bicep or Terraform
Experience with Microsoft Purview data governance, including data cataloging, lineage, sensitivity labels, and data quality
Experience preparing data for AI workloads, including Azure AI Search indexing, Microsoft Foundry solutions, or RAG pipelines
Experience with complementary non-Microsoft tools such as dbt, Apache Airflow, Apache Kafka, or Snowflake
Experience with Microsoft Power Platform, including Power Apps, Power Automate, and Dataverse, and integrating low-code solutions with governed enterprise data
Experience using AI coding agents such as Claude Code or Codex to accelerate pipeline development safely
Relevant certifications or credentials, such as:
Microsoft Certified: Fabric Data Engineer Associate (DP-700)
Microsoft Certified: Azure Databricks Data Engineer Associate (DP-750)
Microsoft Certified: Fabric Analytics Engineer Associate (DP-600)
Microsoft Certified: Power Platform Fundamentals (PL-900)
Databricks Certified Data Engineer Associate or Professional
Experience operating in a regulated, security-sensitive, government, or defense-adjacent environment

The total compensation for this role can vary from $133,000 to $166,000 a year based on individual qualifications. Eligibility for yearly discretionary bonus. Persistent Systems, LLC offers a comprehensive benefits package including medical, dental, vision, life, and disability insurance; paid time off (sick time and vacation time); flexible spending accounts; 401(k) plan with company match; fitness membership reimbursement; tuition assistance; mental health benefits; and pet discounts.

Company Overview

Headquartered in New York City since 2007, Persistent Systems, LLC is a global communications technology company which develops, manufactures, and integrates a patented and secure Mobile Ad Hoc Networking (MANET) system: Wave Relay®. The company’s industry leading R&D team has designed wireless networking protocols to support their cutting-edge Wave Relay® system and technology. Wave Relay® is capable of running data, video, voice and other applications under the most difficult and unpredictable conditions. Their suite of products is field proven and utilized in Commercial, Military, Government, Industrial, Agriculture, Robotics, and Unmanned System markets.

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More about this job

Skills

Data models
Reporting
Microsoft Power BI
SQL
PySpark
Data engineering
Microsoft Azure Data Factory
Operational databases
Apache Spark
Microsoft Azure
Databricks
Unity
Account reconciliation
Root cause analysis
Cataloging
Git
IBM Terraform
Microsoft SQL Server
Python
API development
Microsoft PowerShell
GitLab
Atlassian Bitbucket
Google Analytics
Microsoft Power Platform
Power Apps
Microsoft Power Automate
dbt
Apache Airflow
Apache Kafka
Snowflake
Data warehousing
Computer science
Process data
Scheduling
Data warehouse
CI/CD
Troubleshoot
DevOps
DP

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