Datronix Solutions LLC
Website:
datronixsolutions.net
Company:
https://www.linkedin.com/company/datronix-solutions-llc
Seniority: Mid-Senior level
Industries: IT Services and IT Consulting and Oil and Gas
Job details:
The Enterprise Data Architect, Lead plays a crucial role in designing, implementing, and maintaining data integration solutions within our organization. This role collaborates with customers and cross-functional teams to ensure seamless data pipelines from customers’ systems. The expertise for this role will contribute to the organization’s overall strategy and architecture for data acquisition.
Job Duties
- Source System Extraction (This Is the Core of the Role)
- Independently extract data from industrial source systems including OSIsoft PI historians, SAP PM/EAM, Maximo, eMaint, lab/LIMS systems, and other CMMS/ERP platforms.
- Navigate customer IT environments to establish connectivity — VPNs, service accounts, firewall rules, read-only database access — often with limited or no documentation.
- Reverse-engineer undocumented or poorly documented source schemas to identify the right data for integration.
- Build and own the extraction layer: connectors, API calls, direct database queries, file-based ingestion from heterogeneous client environments.
- Handle the reality that every customer’s data is messy in a different way — inconsistent tag naming, mismatched equipment IDs, unmaintained asset hierarchies.
- Data Transformation and Pipeline Development
- Design, build, and maintain data pipelines that clean, transform, and load extracted data into our reliability platform.
- Develop integration architecture and blueprints tailored to each customer’s source system landscape.
- Implement data quality checks, reconciliation processes, and monitoring to ensure ongoing accuracy.
- Build and maintain master data mapping strategies — including change management processes as clients execute MOCs, add equipment, or decommission assets.
- Own pipeline monitoring, alerting, and uptime SLAs for all production data extraction and integration systems. These are live production pipelines serving customers — when extraction fails, you are responsible for detecting the failure, diagnosing the root cause, and restoring the data flow within SLA.
- Client Communication and Technical Leadership
- Serve as the primary technical point of contact with customer IT teams for all data access and connectivity matters.
- Respond to detailed technical inquiries from client IT leadership (architecture questions, data mapping strategies, security concerns) with clarity and confidence.
- Lead discovery sessions with customers to understand their source systems, data flows, and integration requirements.
- Create and maintain architecture documentation, integration runbooks, and data dictionaries for each client engagement.
- Provide technical guidance and mentorship to team members and drive knowledge sharing across the data engineering team.
- Manage integration project plans, timelines, and deliverables across multiple concurrent client engagements. Drive accountability on milestones, coordinate dependencies with client IT teams, and ensure integrations are completed on schedule.
- Strategy and Team Building
- Lead the enterprise data integration strategy and platform architecture across the organization.
- Provide new ideas and approaches to the CTO and enterprise architecture team on data acquisition and integration best practices.
- Drive recruitment to build and grow a high-performing data engineering team.
- Continuously evaluate and adopt emerging data technologies and practices.
Accountabilities/Results/Success for this role
- Successful design and deployment of scalable and secure data architectures and data pipelines.
- Enhanced data quality, efficiency, and accessibility across the organization.
- Effective execution of data integration projects, demonstrating strong project management skills and consistent delivery on time and within scope.
- Continuous improvement and adoption of emerging data technologies and practices.
- Creation of innovative, customer-focused data solutions that set the organization apart and add measurable value.
Measures
Percentage of projects delivered on time: 80%
Team utilization: 80%
Data Solutions: 3
Production pipeline uptime: 99.5%
Required Qualifications/Skills/Competencies
- Hands-on experience extracting data from at least one of: OSIsoft PI, SAP PM/EAM, Maximo, eMaint, or similar industrial/operational systems. This is non-negotiable.
- Experience in oil and gas, refining, chemicals, or heavy industry environments.
- Direct experience working with customer or client IT teams to negotiate and establish data access (firewall rules, VPN connectivity, service accounts, API credentials).
- SQL proficiency — specifically the ability to explore unfamiliar database schemas and write extraction queries with little or no documentation.
- Python for data extraction, transformation, and pipeline automation.
- Experience with cloud-based data integration (Azure Data Factory, Azure Functions, or comparable).
- Strong knowledge of data integration patterns, ETL/ELT, APIs, and messaging protocols (REST, SOAP, OPC).
- Demonstrated experience with enterprise database technologies and data modeling.
- Excellent communication skills — you’ll be the person answering detailed technical emails from client IT directors and leading discovery calls
Preferred Qualifications
- Familiarity with reliability engineering concepts (RBI, CMMS workflows, asset hierarchy management, inspection data).
- Experience with Cognite Data Fusion (CDF) or similar industrial data platforms.
- Knowledge of PI Web API, PI SDK, or AF SDK for historian data extraction.
- Experience with OPC-UA/DA protocols for real-time industrial data.
- Background in data governance and compliance measures.
- Understanding of microservices architecture and containerization (Docker, Kubernetes).
- Experience with DevOps tools and practices (Azure DevOps, CI/CD pipelines
Equipment and Software Knowledge
- Expertise in data integration tools and platforms (e.g., Azure Data Factory, Informatica, Talend).
- Proficiency in big data platforms (Hadoop, Spark, etc.) and analytics tools (Power BI, Tableau).
- Familiarity with DevOps tools and practices (e.g. Azure DevOps).
Direct Reports
Data Engineers will report to this role
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