Data Engineer

Responsibilities

• Build and validate all T1 and T2 connector integrations per the architecture specifications in the Enterprise Architecture Document and FSD; execute integration builds per FDE direction.
• Construct and operate data pipelines: source connection, transformation logic, normalization rule implementation, DQ check configuration, and dashboard data feed maintenance; own the operational reliability of all data pipelines throughout the SOW term.
• Build and maintain ETL jobs for all confirmed T1/T2/T3 connector sources; handle schema changes, API version updates, and data quality remediation as they arise in production.
• Implement MCP server configurations under the FDE's technical direction; build and test MCP endpoint integrations that enable AI agents to access client operational systems in real time.
• Support the Technical PM on FSD Section 5 (technical integration specifications): provide implementation-level detail, document field mappings, normalization rules, and data flow specifications for each connector built.
• Maintain data flow planograms as a living technical record; update planograms whenever a connector is modified, a normalization rule changes, a schema mapping is revised, or a new integration is added.
• Assess new connector requests per the LOE threshold framework; provide effort estimates to the Technical PM within five business days of any connector request.
• Post Day 90, transition from foundation build to live platform operations; own production data pipeline reliability, data quality monitoring, and Routine connector builds as the client requests new data connections.
• Onboard and train incoming Data Engineers and BI Developers as they are activated via Change Order; ensure continuity of pipeline operational standards and FSD compliance.
• REQUIRED QUALIFICATIONS
• 4-7 years of experience in data engineering, ETL development, or enterprise systems integration.
• Proficiency building and operating data pipelines at scale using modern ETL frameworks and tools.
• Hands-on experience with SAP integration methods (BAPI for ECC and/or OData for S/4HANA); experience integrating with SD, QM, and BW modules preferred.
• Strong proficiency with REST API integrations, OAuth2 authentication, and field mapping/normalization implementation.
• Experience building and maintaining data quality check frameworks; ability to remediate schema variance, missing fields, and encoding issues in production pipelines.
• Familiarity with EDI (X12/AS2) data ingestion and carrier milestone event normalization.
• Ability to produce and maintain data flow documentation (planograms, data dictionaries, field mapping registers).
• Proficiency with at least one cloud data platform (AWS, GCP, or Azure) and associated data pipeline tooling.
• Bachelor's degree in Computer Science, Information Systems, Data Engineering, or a related field; or equivalent professional experience.

Requirements

• Experience with MCP (Model Context Protocol) server configuration and AI agent real-time data access integration.
• Background in Salesforce (SFDC) REST/Bulk API 2.0 data pipeline construction.
• Familiarity with IoT data stream ingestion and real-time event processing.
• Experience integrating with Power BI REST API and/or embedded BI data feed architectures.
• Knowledge of data normalization requirements for multi-region, multi-timezone operational data sets (UTC standardization, currency normalization, carrier name resolution).
• Salary Range

Benefits

Additional Information

POSITION OVERVIEW The Data Engineer is responsible for the integration build, data pipeline construction and operations, connector implementation, ETL development, and Astreya proprietary data pipeline operations that underpin Astreya's Intelligence Suite deployment at a strategic enterprise technology client. Working under the technical direction of the Forward Deployed Engineer and alongside the Technical Project Manager, this individual executes the integration builds that the FDE architects - enabling the FDE to focus on platform governance, AI Agent design, and client IT engagement. The Data Engineer is also the operational owner of all live data pipelines throughout the engagement term.

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Company Intel

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