Data Engineer (Devops)

About Us

Perficient is the global AI-first consultancy. Our team of strategists, designers, technologists, and engineers partners with the world’s most innovative enterprises and admired brands to deliver real business results through the transformative power of AI. As part of our AI-First strategy, we empower every employee to build AI fluency and actively engage with AI tools to drive innovation and efficiency. We break boundaries, obsess over outcomes, and shape the future for our clients. Join a company where bold ideas and brilliant minds converge to redefine what’s possible - while building a career filled with growth, balance, and purpose.

🧠 Role Area 🔧 Responsibilities

DevOps Engineer

Automate deployment. Manage CI/CD pipelines for Neo4j updates and configuration changes

Data Engineer

Design and implement data ingestion pipelines from Azure Databricks services. Transform and model data for graph structures

Graph Security Strategy

Use the structure and metadata of the graph—labels, relationship types, and property keys—to enforce fine-grained access control and ensure data integrity

Performance \& Monitoring Lead

Monitor Performance, Neo4j query efficiency. Tune platform configurations and optimize Cypher queries

Execute Graph Data Scientist algorithms

Use Neo4j Graph Data Science (GDS) for advanced analytics. Apply algorithms like PageRank, community detection, and similarity search

Visualization Specialist

Implement Neo4j Bloom to visualize and explore graphs, enabling validation of graph representations with business stakeholders.

💡 Skill/Tool

Core Technical Skills

Python Scripting ETL logic, interacting with Neo4j via drivers like neo4j or py2neo

SQL Querying and transforming data from relational sources

Apache Spark Distributed data processing in Azure Databricks

Cypher Neo4j’s query language for graph modeling and manipulation

Cloud \& Platform Skills

Azure Databricks Managing clusters, notebooks, Delta Lake, and job orchestration

Azure Data Lake Storage (ADLS) Ingesting and managing structured/unstructured data

Azure Data Factory (ADF) Orchestrating pipelines across services

Graph Data Engineering

Graph Modeling Concepts Designing nodes, relationships, and properties, Organizing

Neo4j Integration Using drivers or REST APIs for ingestion

Data Engineering Practices

ETL/ELT Pipeline Design Building scalable, fault-tolerant pipelines

Data Governance \& Security Managing access and sensitive data across cloud services

Monitoring \& Optimization Tuning Spark jobs and Neo4j queries

Bonus Skills

Version Control \& DevOps Git, GitHub Actions, Azure DevOps for CI/CD

Visualization Tools Power BI, Neo4j Bloom for graph exploration

Certifications Azure DP-203, Databricks Data Engineer Professional