About the role
ESSENTIAL JOB FUNCTIONS:
Lead the definition and evolution of the enterprise data platform strategy by developing, maintaining, and continuously evolving architecture standards, reference architectures, and design patterns aligned with business priorities, analytical requirements, and AI/ML enablement across data and ETL/ELT platforms.
Develop, maintain, and steward enterprise architecture artifacts and documentation—including current and target state designs, diagrams, standards, and decision records—ensuring accuracy, consistency, and accessibility within the architecture repository.
Evaluate, recommend, and govern the selection of data platforms and ETL/ELT tools, balancing scalability, performance, security, cost, and operational maturity while reducing unnecessary platform sprawl.
Review and guide data platform and pipeline designs to ensure consistency with enterprise standards, scalability requirements, and architectural best practices.
Provide technical leadership and guidance to data engineering teams, analytics teams, platform product owners to translate architecture standards into implementable solutions and influence technical direction across the organization.
Define architectural guardrails that ensure data platforms can scale reliably, meet performance expectations, and support high availability and disaster recovery requirements.
Establish architectural standards for platform operations, environment management, reliability, observability, deployment automation, and lifecycle management to ensure data platforms are production-ready and supportable.
Ensure data platform architectures incorporate security controls, data privacy requirements, and regulatory compliance (e.g., access controls, encryption, data lineage, and retention) by default.
Define and standardize ETL/ELT and data integration patterns, including batch, streaming, and event driven approaches, to support analytics, operational reporting, and downstream consumption.
Participate in enterprise architecture governance forums, providing clear architectural guidance and decision support for data platform and integration initiatives.
Incorporate financial discipline into platform architecture by guiding cost efficient design, platform right sizing, and lifecycle management in partnership with FinOps and platform teams.
Continuously assess emerging data platform and ETL technologies, recommending updates to standards and strategy as capabilities and business needs evolve.
Drive innovation in AI-powered data engineering workflows and automation.
• Perform other duties as assigned.
Requirements
Bachelor’s degree in Information Technology, Data Science, Computer Science, or related field; or an equivalent combination of education and experience.
7+ years in database engineering with 3+ years in database architecture design and support.
Preferred Education, Additional Qualifications and Experience
Master’s degree in Information Technology, Data Science, Systems Engineering, or Computer Science
Familiarity with healthcare payer data domains such as claims, eligibility, provider, member, care management, utilization management, or regulatory reporting.
Experience designing API‑based data integration using Apigee
Integration with enterprise systems including Salesforce, FACETS, and ServiceNow
Required Knowledge, Skills, and/or Abilities
Experience architecting enterprise data platforms across cloud environments (Azure, Google Cloud).
Deep knowledge of Snowflake as an enterprise data warehouse platform.
Hands-on familiarity with modern data platforms and services (e.g. Azure Data Lake/ADLS, Fabric, Databricks, SQL Server, PostgreSQL, Oracle).
Hands‑on experience with ETL/ELT orchestration platforms (Matillion).
Strong hands‑on technical fluency with SQL and Python sufficient to prototype, review optimize, and guide engineering implementation of data pipelines and transformations.
Ability to design analytics‑ready datasets that support reporting, operational analytics, and AI/ML use cases.
Experience with data architecture patterns, including layered architectures (ingestion, transformation, repository, consumption).
Experience with data modeling, analytics platforms, and data governance concepts.
Ability to design data platforms with security controls by default, including access controls, encryption, data lineage, and retention.
Experience integrating internal and external data sources using secure, governed ingestion methods.
Ability to translate architectural standards into implementable solutions with engineering teams.
Experience enabling AI/ML workloads through scalable, well‑governed data platforms.
Familiarity with infrastructure‑as‑code concepts, automation, and CI/CD for data platforms.
Understanding of observability, reliability, and operational readiness for data pipelines and platforms.
Ability to evaluate emerging data technologies and recommend adoption based on maturity, risk, and business value.
Communicate complex technical concepts to engineering with good diagramming hygiene for mutual understanding.
Proficient communication and presentation skills for inter-level audiences.
Comfortable in agile or waterfall methodologies.
Originally posted on Himalayas
