AI Summary
Lead data development efforts in a team lead or lead engineer capacity. Responsible for data engineering, analytics enablement, and data governance support. Strong hands-on expertise in SQL development, data modeling, and data integration patterns.
Key Highlights
Lead design and implementation of data ingestion, transformation, and persistence patterns
Develop and maintain robust, testable data pipelines with appropriate observability
Partner with application teams to define and implement reliable data interfaces
Key Responsibilities
Leading design and implementation of data ingestion, transformation, and persistence patterns aligned to a defined solution architecture
Developing and maintaining robust, testable data pipelines with appropriate observability (logging, monitoring, alerting)
Establishing development standards for data code including SQL, transformations, and orchestration, with peer review and performance tuning practices
Partnering with application teams to define and implement reliable data interfaces including APIs, extracts, and event or message-based integrations
Producing and maintaining data documentation including pipeline specifications, data dictionaries, lineage summaries, and data quality rule documentation
Shaping datasets for reporting and operational analytics consumption
Defining and documenting business metrics, calculations, and dataset expectations in collaboration with business stakeholders
Ensuring analytic outputs are traceable to source data and aligned to approved definitions
Supporting data governance objectives including data definitions, business glossary alignment, metadata documentation, and lineage capture
Engaging data owners and stewards in operational governance workflows
Implementing and validating data access controls consistent with security requirements, including role-based access, least privilege, and auditing
Contributing to governance reviews with artifacts such as data flows, controls, data quality rules, and lineage summaries
Identifying data risks early (quality, completeness, timeliness, duplication, referential integrity) and proposing remediation options
Creating practical approaches to manage known data imperfections, including tolerant processing, exception queues, and reconciliation reports
Communicating constraints and tradeoffs clearly, and recommending alternative approaches when requests are not feasible
Technical Skills Required
Benefits & Perks
100% remote
Job Description
Data Architect 100% Remote
Requirements
- NYS utility experience is strongly preferred.
- If candidates do not have New York utility experience, candidates with experience working for other state utilities will also be considered.
Role Overview [10+ Years, Preferably Utilities (NYS) and Data Architect Roles with Azure Databricks and C3.ai including AMI and Customer Data]
- Responsible for data engineering, analytics enablement, and data governance support across the BEUP 3.0 program. This role reports to the Solution/Technical Architect and works closely with product ownership, application engineering, Enterprise Architecture, Cloud Services, and InfoSec.
- Senior-level experience leading data development efforts in a team lead or lead engineer capacity
- Strong hands-on expertise in: SQL development, optimization, and troubleshooting
- Data modeling (conceptual, logical, and physical; dimensional modeling for analytics where applicable)
- Data integration patterns (ETL/ELT, batch and event-driven ingestion)
- Data quality profiling, validation rules, reconciliation, and exception handling
- Experience implementing data solutions in cloud environments, including familiarity with cloud-native data and analytics services (managed databases, object storage, orchestration, streaming and queues, monitoring and logging)
- Demonstrated ability to operate effectively with legacy data, including inconsistent definitions, incomplete normalization, and data quality gaps
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- Leading design and implementation of data ingestion, transformation, and persistence patterns aligned to a defined solution architecture
- Developing and maintaining robust, testable data pipelines with appropriate observability (logging, monitoring, alerting)
- Establishing development standards for data code including SQL, transformations, and orchestration, with peer review and performance tuning practices
- Partnering with application teams to define and implement reliable data interfaces including APIs, extracts, and event or message-based integrations
- Producing and maintaining data documentation including pipeline specifications, data dictionaries, lineage summaries, and data quality rule documentation
- Analytics enablement
- Shaping datasets for reporting and operational analytics consumption
- Defining and documenting business metrics, calculations, and dataset expectations in collaboration with business stakeholders
- Ensuring analytic outputs are traceable to source data and aligned to approved definitions
- Data governance & controls
- Supporting data governance objectives including data definitions, business glossary alignment, metadata documentation, and lineage capture
- Engaging data owners and stewards in operational governance workflows
- Implementing and validating data access controls consistent with security requirements, including role-based access, least privilege, and auditing
- Contributing to governance reviews with artifacts such as data flows, controls, data quality rules, and lineage summaries
- Data quality, risk & issue management
- Identifying data risks early (quality, completeness, timeliness, duplication, referential integrity) and proposing remediation options
- Creating practical approaches to manage known data imperfections, including tolerant processing, exception queues, and reconciliation reports
- Communicating constraints and tradeoffs clearly, and recommending alternative approaches when requests are not feasible
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