Senior Data Architect / Data Engineer
New
J
JobgetherData Engineering
Based in the United States, generally aligned with business needs during standard EST hoursFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 8+ years
- Required Skills
- SQLCloud ComputingGCPGitData engineeringData modeling
Requirements
- 8+ years of experience in data engineering, data architecture, or a related discipline.
- Proven experience designing data architectures and models within complex enterprise environments.
- Strong background with legacy systems, large-scale data migration, and transformation initiatives.
- Advanced understanding of relational data modeling, schemas, normalization, mappings, and data structures.
- Ability to evaluate when a canonical data model is beneficial versus when a flexible approach is more appropriate.
- Strong SQL skills with experience developing scripts for migration, transformation, and validation.
- Experience working with Google Cloud Platform (GCP) and cloud-based data solutions.
- Ability to combine high-level architectural thinking with hands-on technical delivery.
- Strong analytical and problem-solving skills with the ability to organize complex or unclear data environments.
- Experience mentoring engineers and supporting technical team growth.
- Familiarity with Git-based development workflows; GitLab experience is a plus.
- Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related field preferred.
Responsibilities
- Design and guide data architecture strategies for a major transformation initiative involving multiple legacy systems.
- Analyze complex and inconsistent source data structures and determine effective approaches for consolidation, mapping, and transformation.
- Develop unified or semi-unified data models that balance standardization, flexibility, and long-term maintainability.
- Make architectural decisions around canonical models, data relationships, schemas, and source-specific variations.
- Write and deliver scripts supporting data migration, transformation, validation, and data cleanup activities.
- Identify data quality issues, structural gaps, and inconsistencies while implementing practical solutions.
- Collaborate with technical and business teams to understand existing systems and define future-state requirements.
- Establish data engineering standards, patterns, and best practices to support future initiatives.
- Provide technical leadership, guidance, and mentorship to junior and mid-level data professionals.
- Support cloud-based data environments and contribute to scalable, maintainable solutions.
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