Data Engineer with DevOps

Job Title: Senior Data Engineer with DevOps

Duration : Full Time

Location : Pittsburgh, PA, Cleveland, OH, or Dallas, TX

Work Mode : 5 Days Onsite

Years Of Exp : 8+ Yrs



Job Description:

We are seeking a Data Engineer with 5 years of experience to design and maintain scalable data pipeline supporting analytics, reporting, and operational needs. The role involves collaborating with cross functional teams to ensure data alignment with business requirements and enterprise standards.



Duties and Responsibilities:

• Design and build scalable data pipelines aligned with business needs
• Process large dataset (batch + sometimes near Realtime)
• Ensure data quality, consistency, and governance standards across systems
• Support data integration and transformation efforts for analytics and reporting platforms
• Maintain data dictionaries, metadata, and documentation
• Participate in data architecture reviews and model validation processes
• Support analytics reporting and risk platforms.

• 5+ years of experience in data engineering and big data processing
• Strong expertise in Apache Spark (Spark Core, Spark SQL) and PySpark for large scale batch processing
• Experience working with structured and semi structured data, including complex transformations and performance tuning
• Proficiency in data ingestion and integration from sources like Oracle, SQL Server, Hive, HDFS, and S3; transform data into ‘curated data models'
• Experience writing data to Hive tables, Data Lakes (Iceberg), and downstream reporting systems
• Strong knowledge of SQL and data modeling concepts
• Hands on experience with Apache Airflow for workflow orchestration (DAG design, scheduling expectations, monitoring)
• Proficiency in shell scripting for job automation, file validation, dependency handling, and logging. Trigger Spark Jobs, perform file checks and validation; Archive & purge data; mange job dependency, logging & error handling
• Strong understanding of batch processing and batch job scheduling frameworks
• Experience migrating from CA7/Control M Airflow (daily, hourly, weekly schedules) CI/CD for data pipelines
• Fundamentals in Linux and Networking
• Docker, OCP containerization / Kubernetes

• Knowledge of CI/CD pipeline tools: Tools commonly include Jenkins, GitHub Actions, Azure DevOps, GitLab Cl, Maven, and Gradle
• Automate operational tasks using Python, Bash/Shell, and PowerShell
• Implement monitoring and alerting, Application Insights. Enable centralized logging with tools such as ELK.
• Experience ensuring data quality, reliability, and compliance in regulated environments
• Good communication and documentation skillsGood communication and collaboration skills in cross-functional teams
• Agile/Safe methodologies

• Airflow
• Containerization
• DevOps
• Elastic Stack & Elasticsearch
• GitHub
• Hadoop Hive
• Jenkins
• JSON Web Token (JWT)
• Kubernetes
• OpenShift
• Oracle
• Python
• Shell Script
• SQLite