Senior Data Architect / Data Engineer

New

Jahnel GroupSoftware Development

Schenectady, New York, United States, Remote, 9:00 AM to 5:00 PM ESTFull-TimeSenior

Salary not disclosed

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Job Details

8+ years of experience in data engineering, data architecture, or a closely related field.
Strong experience designing data architecture and data models for complex enterprise environments.
Proven experience working with legacy systems and large-scale data transformation or migration efforts.
Strong understanding of relational data modeling, data structures, schemas, mappings, and normalization.
Demonstrated ability to determine when a canonical data model is appropriate and when a more flexible approach is needed.
Strong SQL skills and the ability to quickly write scripts for data transformation and migration.
Experience with Google Cloud Platform (GCP) and cloud-based data environments.
Comfortable working hands-on while also making high-level architectural decisions.
Strong problem-solving skills and the ability to bring structure to ambiguous or messy data environments.
Experience mentoring or advising other data engineers and helping less experienced team members grow.
Experience with Git-based development workflows; GitLab experience is a plus.
Excellent communication skills and the ability to explain complex technical concepts to both technical and non-technical stakeholders.
Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related field is preferred.

Design and guide data architecture for a complex transformation and migration initiative spanning approximately 11 legacy systems.
Analyze highly varied source data, potentially including roughly 400 form variants and source structures.
Develop unified or semi-unified data models that balance consistency, flexibility, and long-term maintainability.
Make thoughtful architectural decisions around canonical data models, mappings, transformations, and source-specific variations.
Write and ship scripts quickly to support data migration, transformation, validation, and cleanup.
Identify data quality issues, inconsistencies, and structural gaps across legacy systems and develop practical solutions.
Collaborate with technical and business stakeholders to understand existing data structures and future-state requirements.
Provide technical guidance and mentorship to junior and mid-level data team members.
Help establish patterns and best practices that reduce rework and support future data initiatives.

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