Senior Data / Solution Architect (AI + Data Architecture)
T
TechtorchEnterprise Technology Consulting
United StatesFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 8+ years
- Required Skills
- DockerPythonSQLApache AirflowKubernetesMLFlowSnowflakeSparkBigQuerydbtDatabricks
Requirements
- 8+ years in data architecture, solution architecture, or data engineering roles
- Proven track record designing and implementing SOO/SOR/CDM architectures, lakehouse/warehouse patterns, and dimensional modeling
- Hands-on experience delivering AI-enabled solutions to production environments (beyond experimentation)
- Cloud implementation experience in Azure, AWS, or GCP (deep expertise in at least one platform)
- Consulting delivery experience across multiple clients and domains preferred
- Strong hands-on proficiency with SQL and Python
- Expert-level experience building data pipelines and orchestration (Airflow, Azure Data Factory, dbt)
- Deep knowledge of distributed processing frameworks (Apache Spark)
- Experience with modern data platforms: Databricks, Snowflake, Azure Synapse, or BigQuery
- MLOps and model deployment exposure (Azure ML, SageMaker, Vertex AI, MLflow, Docker/Kubernetes) highly valued
- Strong analytical and problem-solving skills with attention to architectural patterns and best practices
- Excellent stakeholder management and communication abilities to bridge technical and business audiences
- Ability to balance strategic architecture vision with pragmatic, hands-on implementation
- Collaborative mindset with ability to mentor engineering teams and drive technical excellence
- Adaptability to work in fast-paced PE-backed environments with focus on rapid value delivery
Responsibilities
- Design and implement enterprise data architectures spanning Source of Origin (SOO), System of Record (SOR), and Common Data Model (CDM/Curated) layers.
- Lead core data architecture activities including data modeling (conceptual, logical, physical), master data management, reference data, metadata, lineage, and governance frameworks.
- Build scalable batch and real-time data pipelines and data products leveraging lakehouse and warehouse patterns.
- Architect and support end-to-end deployment of AI/ML solutions on data platforms, including feature engineering, model integration, orchestration, and monitoring.
- Work hands-on with engineering teams to deliver production-grade solutions with proper CI/CD, security, performance optimization, and observability.
- Partner with business stakeholders and technical teams to translate requirements into clean, scalable technical designs that align with PE value creation objectives.
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