10 to 13 years of experience in data architecture or related roles
Advanced proficiency in SQL for designing schemas and writing complex queries
Solid Python programming experience for data processing scripts
Hands-on experience with data warehousing concepts
Requirements:
Design robust enterprise data architectures integrating multiple source systems using SQL and Python
Develop end-to-end data models to ensure high-performance query execution
Coordinate with stakeholders to understand data requirements and translate them into data designs
Implement data transformation workflows using Python and SQL to standardize structures and improve data quality
Job description
Our Client, an IT Services and Consultant company, is looking for an Informatica and Webfocus Lead and Architect for their Remote location.
Responsibilities:
Design robust enterprise data architectures that integrate multiple source systems using SQL and Python to provide reliable and scalable data foundations for analytics and reporting needs.
Develop end to end data models that are logically consistent and physically optimized to ensure high performance query execution and efficient storage utilization across hybrid environments.
Coordinate with product and business stakeholders to understand data requirements and translate them into conceptual and logical data designs that support current and future use cases.
Implement data transformation workflows using Python and SQL that standardize structures, apply business rules, and improve data quality for consumption by downstream applications.
Optimize database structures, indexes, and query patterns using advanced SQL techniques to reduce latency, improve throughput, and enhance overall system resilience.
Create comprehensive data mapping documentation and architecture diagrams that clearly describe lineage, dependencies, and integration points across the enterprise data landscape.
Establish data governance practices in collaboration with governance teams to enforce consistent definitions, naming standards, and access patterns across all data assets.
Collaborate with data engineers and analysts to validate data models, troubleshoot performance issues, and refine designs based on usage feedback and evolving business priorities.
Implement monitoring practices for data pipelines and storage layers using Python based scripts and native database features to detect issues early and prevent data quality incidents.
Guide adoption of best practices for SQL coding, schema evolution, and version control processes so that teams can maintain predictable and auditable changes to data platforms.
Align data architecture decisions with organizational technology strategy by evaluating new tools, frameworks, and patterns that can enhance scalability, security, and cost efficiency.
Support compliance initiatives by ensuring that data models and pipelines respect privacy constraints, retention policies, and appropriate access controls across environments.
Document architectural decisions and rationale in a clear and structured manner so that future contributors can understand design intent and extend solutions safely.
Requirements:
Possess ten to thirteen years of experience in data architecture or closely related roles with strong exposure to large scale enterprise environments and complex integration scenarios.
Demonstrate advanced proficiency in SQL for designing schemas, writing complex queries, and tuning database workloads to support demanding analytical and operational requirements.
Apply solid Python programming experience for building data processing scripts, automation utilities, and integration components that work reliably within hybrid data platforms.
Show practical experience defining conceptual, logical, and physical data models using standard modeling techniques and tools aligned with enterprise architecture practices.
Bring hands on experience with data warehousing concepts such as star schemas, slowly changing dimensions, and fact design to support business intelligence and reporting workloads.
Exhibit familiarity with data governance principles including data quality management, metadata documentation, and standardized data definitions across organizations.
Demonstrate strong communication and collaboration skills to work effectively with cross functional teams in a hybrid work model covering onsite and remote interactions.
Display awareness of cloud based data services and hybrid deployment patterns even when travel is not required so that solutions remain future ready and scalable.
