Overview
On Site
$60 - $80
Accepts corp to corp applications
Contract - W2
Contract - 12 Month(s)
No Travel Required
Skills
Data Architect
Data Pipelines
AWS
Job Details
Title: Data Architect Location: Burbank CA ( Hybrid ) Description:
The Senior Data Architect plays a critical role in designing this foundation ensuring that data models, pipelines, and integration frameworks are scalable, performant, and aligned with enterprise data governance and platform goals.
Embedded within the Platform Pod, the Senior Data Architect partners with the Platform Owner, Cloud Architect, data engineering teams, and product-aligned pods to ensure the architecture supports both immediate product needs and long-term platform evolution.
This role directly enables the delivery of reliable, real-time, and reusable data products across multiple Economics workstreams.
Job Responsibilities / Typical Day in the Role Design Scalable and Consistent Data Architecture
Define and maintain canonical data models, entity relationships, and semantic layer specifications that ensure consistent use of data across products and domains.
Develop and evolve logical and physical data models that support real-time analytics, forecasting, and scenario planning.
Collaborate with product-aligned pods to design domain-aligned data products that are modular, governed, and discoverable.
Build Reusable, Performant Data Pipelines
Architect data pipelines that support both batch and near real-time processing using AWS-native services (e.g., Glue, Kinesis, Lambda, Step Functions).
Guide ingestion, transformation, and enrichment strategies that optimize for resilience, scalability, and lineage traceability.
Work closely with the Cloud Architect to ensure that infrastructure and orchestration layers meet pipeline and data SLAs.
Embed Governance and Stewardship by Design
Partner with enterprise data governance teams to implement standardized metadata, lineage, and access controls using tools such as Lake Formation, Informatica, or Snowflake. Define data quality rules, validation checkpoints, and anomaly detection processes to support trusted analytics and ML pipelines. Contribute to the enterprise data catalog and enable self-service access through secure, well-documented APIs and schemas. Collaborate Across Platform and Product Pods Work with the Platform Owner to define and deliver shared data services and reusable semantic models that support multi-pod alignment. Support data scientists and analysts by enabling ML/AI-ready data pipelines and structuring data to accelerate model development and deployment. Participate in cross-pod architecture planning to coordinate integration strategies, resolve semantic conflicts, and align on domain boundaries. Must Have Skills / Requirements 1) Experience in data architecture and engineering, with a focus on cloud-native data platforms and modern analytics workflows. a. 7+ years of experience; Designing and delivering data architecture for cloud-based platforms, with strong knowledge of AWS (e.g., Glue, Lambda, Step Functions, Lake Formation) and modern tooling (e.g., Snowflake, Databricks, Informatica). 2) Hands-On Pipeline Design and Orchestration a. 7+ years of experience; Experience architecting and optimizing complex data pipelines ensuring performance, resilience, and real-time capabilities; Hands-on experience building batch and streaming pipelines that are performant, resilient, and traceable using orchestration frameworks that support real-time and ML/AI-ready processing. 3) Expertise in Canonical Modeling and Semantic Design a. 7+ years of experience; Proven ability to design scalable, reusable data models and translate them into physical implementations that align with business domains and analytic needs; Deep proficiency in designing canonical and semantic data models, with proven experience aligning data structures to business domains and analytic use cases.
Mindlance is an Equal Opportunity Employer and does not discriminate in employment on the basis of Minority/Gender/Disability/Religion/LGBTQI/Age/Veterans.
The Senior Data Architect plays a critical role in designing this foundation ensuring that data models, pipelines, and integration frameworks are scalable, performant, and aligned with enterprise data governance and platform goals.
Embedded within the Platform Pod, the Senior Data Architect partners with the Platform Owner, Cloud Architect, data engineering teams, and product-aligned pods to ensure the architecture supports both immediate product needs and long-term platform evolution.
This role directly enables the delivery of reliable, real-time, and reusable data products across multiple Economics workstreams.
Job Responsibilities / Typical Day in the Role Design Scalable and Consistent Data Architecture
Define and maintain canonical data models, entity relationships, and semantic layer specifications that ensure consistent use of data across products and domains.
Develop and evolve logical and physical data models that support real-time analytics, forecasting, and scenario planning.
Collaborate with product-aligned pods to design domain-aligned data products that are modular, governed, and discoverable.
Build Reusable, Performant Data Pipelines
Architect data pipelines that support both batch and near real-time processing using AWS-native services (e.g., Glue, Kinesis, Lambda, Step Functions).
Guide ingestion, transformation, and enrichment strategies that optimize for resilience, scalability, and lineage traceability.
Work closely with the Cloud Architect to ensure that infrastructure and orchestration layers meet pipeline and data SLAs.
Embed Governance and Stewardship by Design
Partner with enterprise data governance teams to implement standardized metadata, lineage, and access controls using tools such as Lake Formation, Informatica, or Snowflake. Define data quality rules, validation checkpoints, and anomaly detection processes to support trusted analytics and ML pipelines. Contribute to the enterprise data catalog and enable self-service access through secure, well-documented APIs and schemas. Collaborate Across Platform and Product Pods Work with the Platform Owner to define and deliver shared data services and reusable semantic models that support multi-pod alignment. Support data scientists and analysts by enabling ML/AI-ready data pipelines and structuring data to accelerate model development and deployment. Participate in cross-pod architecture planning to coordinate integration strategies, resolve semantic conflicts, and align on domain boundaries. Must Have Skills / Requirements 1) Experience in data architecture and engineering, with a focus on cloud-native data platforms and modern analytics workflows. a. 7+ years of experience; Designing and delivering data architecture for cloud-based platforms, with strong knowledge of AWS (e.g., Glue, Lambda, Step Functions, Lake Formation) and modern tooling (e.g., Snowflake, Databricks, Informatica). 2) Hands-On Pipeline Design and Orchestration a. 7+ years of experience; Experience architecting and optimizing complex data pipelines ensuring performance, resilience, and real-time capabilities; Hands-on experience building batch and streaming pipelines that are performant, resilient, and traceable using orchestration frameworks that support real-time and ML/AI-ready processing. 3) Expertise in Canonical Modeling and Semantic Design a. 7+ years of experience; Proven ability to design scalable, reusable data models and translate them into physical implementations that align with business domains and analytic needs; Deep proficiency in designing canonical and semantic data models, with proven experience aligning data structures to business domains and analytic use cases.
Mindlance is an Equal Opportunity Employer and does not discriminate in employment on the basis of Minority/Gender/Disability/Religion/LGBTQI/Age/Veterans.
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.