Data Engineer

We are looking for a Data Engineer to join a Chemicals Manufacturing organization in New Jersey in a contract-to-permanent capacity. This role focuses on creating reliable, secure, and scalable data solutions that connect operational and business systems to enterprise reporting, analytics, and advanced data use cases. The position will work across IT and operational environments to support trusted data delivery for functions such as finance, supply chain, operations, commercial teams, and corporate analytics.

Responsibilities:

• Design, build, and maintain large-scale data pipelines that move information from enterprise and operational source systems into structured data layers for analytics and reporting.

• Develop curated and semantic data models that support business intelligence platforms, downstream applications, and data-driven decision-making across the organization.

• Connect manufacturing and operational technology data sources using approved integration methods while coordinating with plant and controls teams to protect system reliability and safety.

• Establish data quality standards through validation rules, automated testing, and monitoring processes that improve accuracy, completeness, and timeliness of delivered datasets.

• Implement observability practices such as lineage tracking, alerting, and operational runbooks to strengthen support processes and reduce production issues.

• Apply security and governance controls including access management, secrets handling, data classification, and retention practices aligned with enterprise policies.

• Automate deployment workflows with Git-based CI/CD processes to promote consistent releases across environments and improve delivery reliability.

• Tune platform performance and resource usage by optimizing processing strategies, scaling approaches, and storage patterns to balance speed and cost.

• Partner with BI, data, and IT stakeholders to define data contracts, document solutions, and provide operational knowledge transfer to internal teams and external partners.

Requirements * At least 5 years of experience building production-grade data engineering solutions with batch and streaming data pipelines.

• Strong hands-on expertise with Python, PySpark, Apache Spark, and SQL for developing and optimizing large-scale data workloads.

• Experience working with Azure-based data platforms such as Databricks, Synapse, Fabric, Data Factory or equivalent pipeline and storage services.

• Familiarity with event-driven and distributed data technologies, including tools such as Apache Kafka and Hadoop ecosystem components.

• Proven ability to implement automated testing, monitoring, and Git-based CI/CD practices within data engineering environments.

• Understanding of enterprise data modeling concepts, including semantic or dimensional design for analytics and reporting use cases.

• Exposure to industrial or operational data environments, historians, or OT integration patterns is highly valued.

• Ability to support downstream business intelligence, API, and advanced analytics use cases through well-structured and governed data products. Technology Doesn’t Change the World, People Do.®

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