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Data Engineer - Data Governance (W2)

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This role is for a Data Engineer - Data Governance (W2) with a 12-month contract, hybrid location in Plano, TX/Boston, MA/NH. Requires 8+ years of Data Governance experience, expertise in Informatica IDMC, Python, SQL, and cloud services like AWS.
🌎 - Country
United States
πŸ’± - Currency
$ USD
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πŸ’° - Day rate
624
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πŸ—“οΈ - Date
April 1, 2026
πŸ•’ - Duration
More than 6 months
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🏝️ - Location
Hybrid
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πŸ“„ - Contract
W2 Contractor
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πŸ”’ - Security
Unknown
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πŸ“ - Location detailed
Plano, TX
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🧠 - Skills detailed
#S3 (Amazon Simple Storage Service) #Snowflake #AWS (Amazon Web Services) #Debugging #GitHub #Classification #Metadata #Documentation #Data Engineering #Cloud #Python #Consulting #Data Governance #Data Ingestion #Data Quality #Quality Assurance #"ETL (Extract #Transform #Load)" #Security #Data Science #Informatica #Lambda (AWS Lambda) #Data Management #Data Security #EC2 #API (Application Programming Interface) #Agile #Monitoring #Automation #Data Access #Datasets #SQL (Structured Query Language)
Role description
Job Title: Data Engineer - Data governance (W2) Duration: 12 Months Contract Location: Hybrid at Plano, TX/Boston, MA/NH (2-3 Days onsite/week) Job Introduction The Enterprise Data & Analytics Technology (EDATA) organization is actively searching for a highly productive Principal Data Governance Engineer of a distributed, dynamic agile team to serve as a technical expert in analysis, design, coding and testing. Under general direction, acts as a technical expert who consults on highly complex projects. This role is responsible for the analysis, development, and execution of complex data governance platform and enablement solutions to support secure, high-quality, well-managed, and discoverable enterprise data for analytics, reporting, and data science. The role will also help establish and evolve governance standards, policies, best practices, procedures, and quality assurance, enabled through Informatica Intelligent Data Management Cloud (IDMC) – Cloud Data Governance and Catalog (CDGC) and its integrations across the data ecosystem. \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ About the job β€’ Builds and designs complex data solutions and capabilities that improve accessibility, efficiency, governance, and quality of data. β€’ Makes recommendations for how to improve data reliability, quality, governance adoption, and operational effectivenessβ€”and drives those recommendations forward. β€’ Collaborates within an agile team to develop, enhance, and scale modern data governance platforms. β€’ Leads with tool expertise and governance practice knowledge (e.g., metadata management, stewardship workflows, glossary concepts, lineage concepts, classification, and governance controls)β€”not solely platform engineering. β€’ Designs and develops governance-focused services and automation to enable metadata ingestion/synchronization, discovery/catalog, classification/tagging, lineage enablement, governance workflow integration, and auditable governance controls. β€’ Applies cloud infrastructure and automation expertise to build resilient, observable, and secure solutions (e.g., monitoring, orchestration, load auditing, data security). β€’ Leads end-to-end development including coding, testing, debugging, and production support; develops automated tests and applies best practices in code quality. β€’ Leads and directs the work of team members and may mentor junior team members. β€’ Support can include but is not limited to: cutover plans, post-implementation support, issues/defects, transition planning, and knowledge sharing efforts. \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ Qualifications β€’ Bachelor or Master’s degree in technical or business discipline or equivalent experience; technical degree preferred. β€’ Minimum 8+ years of Data Governance Engineering experience, including consulting on highly complex initiatives. β€’ Hands-on experience with Informatica IDMC Cloud Data Governance and Catalog (CDGC) or comparable industry data governance/catalog tooling (data discovery/catalog, glossary/metadata management, classification, lineage enablement, governance workflows). β€’ Experience establishing and operationalizing data governance standards, policies, best practices, procedures, governance controls, and quality assurance (e.g., stewardship/ownership patterns, metadata standards, data quality expectations and monitoring, auditability). β€’ Proficient in data engineering languages and tools, including Python and SQL; knowledgeable in new and emerging technologies. β€’ Understanding of code management practices and CI/CD (e.g., GitHub) and experience working in agile delivery models. β€’ Strong understanding of layered solutions and designs, including security considerations. β€’ Highly developed negotiation, consensus building & influencing skills and adaptability to respond to change quickly. β€’ Exceptional oral and written communication skills; proven ability to collaborate effectively with all levels of the organization. β€’ Uses GenAI to improve day-to-day engineering effectiveness (e.g., accelerating research, troubleshooting, documentation, test creation, and routine code tasks) while following security and responsible-use guidelines. β€’ Acts as an advocate for GenAI-enabled governance, identifying and driving practical use cases where GenAI can accelerate enterprise outcomes (e.g., improving data discovery and usability, assisting with metadata and glossary enrichment, supporting classification recommendations, and scaling stewardship workflows). β€’ Preferred Qualifications β€’ Practical experience with AWS core services (CloudFormation, S3, EC2, Lambda). β€’ Understanding of API design concepts and best practices. β€’ Familiarity with using data governance as part of data access controls (e.g., governed datasets, policy/attribute-based controls concepts, request/approval/audit workflows, privacy-driven access patterns). β€’ Experience with Snowflake or similar cloud data platforms. β€’ Experience applying GenAI/LLM capabilities to engineering and/or data governance use cases (e.g., prompt engineering, evaluation/quality approaches, workflow integration, and governance/security considerations for enterprise adoption).