IT Database Administrator-Level 3 - Senior (11 - 15 Years)

Senior Data Engineer - Job Description Role Overview We are seeking a highly skilled and technically versatile Senior Data Engineer to join the NEER IT Salesforce and Enterprise Data Platform team. This role goes beyond traditional data engineering - it sits at the intersection of enterprise data architecture, complex system integrations, AI-augmented delivery, and platform modernization. The ideal candidate brings deep hands-on experience in data pipeline design, multi-system integration, and Atlassian on-premises to cloud migration , while embracing Generative AI and Agentic AI tools as a core part of their daily engineering workflow - driving quality, velocity, and measurable business impact. Key Responsibilities Data Engineering & Architecture Design, build, and maintain scalable, high-performance data pipelines, ETL/ELT frameworks, and data transformation workflows across enterprise platforms including Salesforce, SAP, MuleSoft, and legacy systems Own data modeling, schema design, object relationships, and master data governance standards across multi-system environments - ensuring data integrity, lineage traceability, and audit readiness Lead data migration efforts from legacy platforms to modern cloud-based systems - including transformation logic, validation frameworks, reconciliation protocols, and cutover execution planning Define and implement data quality rules, deduplication strategies, and data lineage standards aligned with NERC compliance, utility-sector regulatory requirements, and enterprise governance frameworks Collaborate with the Enterprise Data Platform (EDP) and Salesforce Data Cloud teams to harmonize data structures, evaluate platform trade-offs, and ensure consistent data availability across the enterprise Integration Engineering Architect and implement data integrations connecting enterprise platforms including Salesforce, MuleSoft, SAP, Documentum, Excalibur, and legacy systems using REST/SOAP APIs, event-driven patterns, CDC (Change Data Capture), and middleware orchestration Build and maintain real-time and batch integration patterns with robust error handling, retry logic, observability frameworks, and logging standards aligned with enterprise resilience requirements Collaborate with MuleSoft Architects and Salesforce Tech Leads to define data contracts, validate end-to-end data flows, and ensure integration layers are testable, auditable, and production-hardened Support multi-system workflow development spanning case management, document handling, and regulated utility operations - ensuring data consistency and traceability across all integration touchpoints tlassian On-Premises to Cloud Migration Lead and execute Atlassian on-premises to cloud migration efforts including Jira, Confluence, and associated tooling - covering discovery, dependency mapping, data extraction, transformation, and cloud cutover planning Assess current on-premises Atlassian configurations, custom plugins, workflows, and data volumes to develop a risk-informed, phased migration strategy with minimal business disruption Design and execute data migration scripts, validation checkpoints, and reconciliation frameworks to ensure completeness, fidelity, and integrity of migrated Atlassian project data, boards, pages, and attachments Collaborate with DevOps, IT infrastructure, and delivery teams to align Atlassian cloud configurations with enterprise security standards, SSO/SAML requirements, and access governance protocols Document migration runbooks, rollback procedures, and post-migration support plans - ensuring audit-ready, traceable, and repeatable migration execution aligned with change management standards Generative AI & Agentic AI Delivery Leverage Agentic AI platforms (Windsurf, Devin, or equivalent) daily for accelerated pipeline development, code generation, data transformation scripting, test automation, and technical documentation Apply prompt engineering best practices - iterative prompting, session scoping, and peer review gates - to ensure AI-generated code and data artifacts are clean, complete, and production-ready Identify and remediate AI-generated code smells, output inconsistencies, and token management issues before delivery - upholding quality standards in an AI-augmented engineering workflow Integrate Einstein AI, Agentforce, and GenAI-enabled data workflows into enterprise automation, case management, and portal data layers under the guidance of the Platform Architect Contribute to team-level AI efficiency benchmarks including development velocity gains, pipeline automation improvements, and sprint throughput aligned with NEER's AI-first delivery strategy for 2026-2028 Governance, Security & Compliance Ensure all data solutions adhere to NERC compliance, utility-sector regulatory standards , and audit-ready documentation requirements - maintaining data lineage and access traceability at all times Implement and enforce data governance protocols including field-level security, role-based data access, encryption standards, and sensitive data handling policies Support security reviews, vulnerability assessments, and data platform audits - contributing to a compliant, observable, and well-governed data engineering practice Maintain thorough technical documentation for all pipelines, integrations, migration artifacts, and data architecture decisions Collaboration & Agile Delivery Partner with the Salesforce Architect, Tech Lead, Product Owner, and Business Analysts to define data requirements, shape acceptance criteria, and deliver sprint-ready, high-quality data engineering work items Participate actively in Agile ceremonies - sprint planning, backlog grooming, daily standups, and retrospectives - as a reliable, communicative, and technically credible delivery contributor Collaborate with QA leads to define data validation test strategies, support SIT/UAT, and drive defect resolution - maintaining defect counts below the 5% threshold across platform releases Mentor junior data engineers on pipeline best practices, AI tooling adoption, and data quality standards - contributing to team capability growth and knowledge sharing across onshore and offshore delivery models Required Qualifications 5+ years of hands-on data engineering experience with demonstrated ownership of enterprise-scale, production-grade data solutions Deep expertise in ETL/ELT design, data modeling, pipeline development , and transformation frameworks across cloud and on-premises environments Proven experience with enterprise system integrations using REST/SOAP APIs, event-driven architecture, CDC patterns, and middleware platforms such as MuleSoft Hands-on experience with Atlassian on-premises to cloud migration - including Jira and Confluence data migration, plugin assessment, workflow migration, and cutover execution Proficiency with cloud data platforms such as Salesforce Data Cloud, Snowflake, Azure Data Factory, or equivalent enterprise data platform tooling Active daily use of Generative AI and/or Agentic AI tools in a data engineering delivery context with measurable quality and productivity outcomes Proficiency with Git, CI/CD pipelines , and DevOps practices applied to data engineering workflows Strong SQL, Python, and scripting proficiency for data transformation, validation, and automation Preferred Qualifications Experience in regulated utility, energy, or infrastructure sectors with awareness of NERC compliance, audit requirements, and data governance standards Familiarity with Salesforce Data Cloud, Einstein Analytics, or Agentforce and AI-enabled data workflow design Hands-on experience with Windsurf, Devin , or similar Agentic AI platforms in a team delivery context Knowledge of Documentum, Excalibur , SAP, or enterprise content management systems from a data engineering perspective Experience with Atlassian cloud administration , Atlassian Marketplace evaluation, and post-migration platform governance Background in multi-vendor offshore/nearshore delivery models and distributed agile team collaboration Familiarity with MuleSoft integration platform and event-driven data architecture patterns EEO: "Mindlance is an Equal Opportunity Employer and does not discriminate in employment on the basis of - Minority/Gender/Disability/Religion/LGBTQI/Age/Veterans."