Enterprise Data Architect, remote | 1058464

Job DescriptionOUR GOAL: Treat our consultants and clients the way we would like others to treat us!Interested in joining our team? Check out the opportunity below and apply today!The Enterprise Data Architect in this contract-to-hire role is responsible for designing, implementing, and maintaining the overall data architecture of the organization. This role involves creating a comprehensive data strategy to support the business's strategic goals, ensuring data consistency, integrity, and availability across various systems. The ideal candidate will have extensive experience in data architecture, data modeling, and data management, with a strong understanding of business intelligence (BI), data analytics, Lakehouse architecture, and technology.Preferred experience: 10+ yearsKey Roles & Responsibilities :Data Migration Design and Technical Oversight:Discovery and Assessment – Understand what data exists and how it behaves.Migration Strategy & Planning – Define how migration will happen.Data Mapping and Transformation Design – Translate source data into target structures.Data Cleansing & Enrichment – Fix data before moving it.Migration Architecture & Pipeline Design – Design the technical movement of data.Data Migration Development & Testing – Build and validate pipelines.Data Reconciliation & Validation – Ensure migrated data is correct.Cutover Execution – Move into productionData Strategy Development:Develop and execute the enterprise data architecture strategy aligned with the organization's goals.Collaborate with business leaders to understand data needs and ensure the architecture supports business objectives.Evaluate and recommend data management tools and technologies that align with the organization's strategic vision.Implement master data management, reference data management, metadata management strategies to ensure data consistency, quality and security.Data Governance and Compliance:Develop and Implement data governance policies and standards, as well as performance indicators and quality metrics, to manage data effectively and ensure compliance with data-related policies and standards.Monitor data quality and performance metrics, addressing issues as they arise to maintain data integrity.A rchitectural Design:Design and implement data models, data flows, and data integration strategies to support business processes.Develop and maintain comprehensive data architecture documentation, including data models, data dictionaries, and metadata.Establish data governance frameworks and best practices to ensure data quality, consistency, and security.Lakehouse Architecture:Design and implement Lakehouse architectures that combine the features of data lakes and data warehouses, optimizing for both structured and unstructured data.Utilize Lakehouse platforms and tools to integrate, store, and analyze large volumes of data efficiently.Evaluate and recommend Lakehouse solutions and technologies, including Delta Lake, Apache Hudi, MS Fabric, Databricks, or Apache Iceberg, to enhance data processing and analytics.Business Intelligence (BI) Integration:Design and implement BI architecture to support reporting, analytics, and decision-making processes.Develop and maintain BI data models, dashboards, and reports that provide actionable insights to business stakeholders.Evaluate and recommend BI tools and technologies to enhance data visualization and analysis capabilitiesCollaboration and Leadership:Lead cross-functional teams to drive data-related projects and initiatives.Communicate data architecture strategies and solutions to stakeholders at all levels, including executives.Mentor and provide guidance to junior data architects and data management staff.Must-have5 years' experienceBS or equivalent experienceAdvanced SQL + data modelingCloud data platform expertiseETL/ELT and pipeline designData governance & securityStrong differentiatorsReal-time/event-driven architectureDataOps / automationData mesh / modern architecture patternsAI/ML data infrastructure and applicationData observability platformsData Architecture & ModelingCore foundation skillConceptual, logical, and physical data modelingDimensional modeling (star/snowflake schemas)Normalization vs. denormalization tradeoffsData vault modeling (increasingly important in modern architectures)Master Data Management (MDM) conceptsToolsER/Studio, ERwin, Lucidchart, SQL DB toolsCloud Data Platforms (Critical Today)Modern architectures are cloud-first.Deep expertise in at least one major cloud:o Azure (Synapse, Data Factory, Fabric)o AWS (Redshift, Glue, Lake Formation)o Google Cloud (BigQuery, Dataflow)Understanding of:o Data lakes vs. lakehouseso Distributed storage (S3, ADLS)o Serverless vs provisioned architecturesData Integration & Pipeline DesignDesigning reliable data movement is central.ETL / ELT design patternsBatch and real-time streaming architecturesChange Data Capture (CDC)API-based integrationEvent-driven architectures (Kafka, Event Hubs)ToolsInformatica, Talend, Azure Data Factory, dbt, Airflow, Python, SparkDatabases & Storage TechnologiesA senior architect should be multi-model.Relational databases (SQL Server, Oracle, PostgreSQL)NoSQL (MongoDB, Cassandra, DynamoDB)Data warehouse platformsData lake / lakehouse architectures (Delta Lake, Iceberg)SkillsQuery optimizationIndexing strategiesPartitioningPerformance tuningData Processing & EngineeringHands-on understanding (even if not coding daily).SQL mastery (must-have)Python or Scala (for pipelines)Spark (critical for large-scale processing)Familiarity with distributed computing conceptsAnalytics & BI Ecosystem UnderstandingNot just pipelines—how data is used.Data warehousing conceptsSemantic layers and data martsBI tools (Power BI, Tableau, Looker)Query performance design for analytics workloadsData Governance, Security & ComplianceA major differentiator at senior level.Data governance frameworksData lineage and metadata managementData catalog tools (., Purview, Collibra, Alation)Security:o Encryption (at rest/in transit)o RBAC/ABACo Data masking / tokenizationRegulatory awareness (GDPR, HIPAA,Architecture Patterns & Design SkillsThis is what separates senior from mid-level.Designing:o Data mesh vs data warehouse vs data fabric architecturesMicroservices & domain-driven design (data implications)Scalability and high-availability designCost optimization patterns in cloudDevOps & DataOpsModern data environments require automation.CI/CD pipelines for data (., Azure DevOps, GitHub Actions)Infrastructure as Code (Terraform, ARM templates)Version control (Git)Monitoring & observability (data pipelines + quality)Data Quality & ObservabilityEnsuring trust in data.Data validation frameworksData quality rules and monitoringObservability tools (Monte Carlo, Great Expectations)Root cause analysis of data issuesMetadata, Lineage & CatalogingCritical for enterprise-scale environments.Data lineage tracking (end-to-end)Business glossariesMetadata management systemsImpact analysis capabilitiesEmerging & Advanced Skills (High Value)Increasingly expected at senior levels.AI/ML data pipelines (basic understanding)Feature storesReal-time analyticsGraph databases and knowledge graphsData products (product thinking applied to data)Reference: 1058464 Don't meet every single requirement? Studies have shown that women and people of color are less likely to apply to jobs unless they meet every qualification. At Revel IT, we are dedicated to building a diverse, inclusive, and authentic workplace, so if you're excited about this role, but your experience doesn't align perfectly with every qualification in the description, we encourage you to apply anyway. You might be the right candidate for this or our other open roles!Revel IT is an Equal Opportunity Employer. Revel IT does not discriminate on the basis of race, religion, color, sex, gender identity, sexual orientation, age, non-disqualifying physical or mental disability, national origin, veteran status or any other basis covered by appropriate law. All employment is decided on the basis of qualifications, merit, and business need.#gdr4900Job ID:1058464