Data Engineer

• Design, build, and productionize reliable, scalable, and secure data pipelines and data products in Azure Databricks that support AI, analytics, and operational use cases.
• Lead the transformation of raw data from multiple source systems into trusted, well-structured datasets suitable for downstream analytics, model development, and AI enablement.
• Drive improvements in internal processes, delivery patterns, and technical solutions that support broader functional strategy and increase team efficiency.
• Solve complex and non-standard data engineering problems using advanced analytical and problem-solving techniques, with strong ownership and sound technical judgment.
• Partner closely with data scientists, analysts, software engineers, product stakeholders, and business teams to ensure data is accessible, trustworthy, and aligned to business outcomes.
• Support AI and data science use cases by enabling high-quality feature-ready data, experimentation workflows, and scalable patterns for model development and deployment.
• Contribute to technical direction within the team, influence key engineering decisions in their area, and act as a go-to resource for peers and less experienced engineers.
• Mentor team members through technical guidance, knowledge sharing, and strong engineering practices consistent with Level 7 expectations.

Senior Data Engineer

• Become a healthcare insurance subject matter expert with sufficient technical skills to produce proof-of-concept solutions to various issues within the industry.
• Perform data cleaning and analysis using SQL, Python, and AI models.
• Analyze big data from diverse sources to identify trends, address gaps, and discover improvement opportunities that align with improving the healthcare system.
• Provide subject matter expertise in identifying product uses and designing strategies that address significant business and technical challenges.
• Gather and translate business requirements into analytical applications and advanced dashboard visuals, reports, or other alert systems.
• Document code using documentation tools and participate in collaborative team code reviews.
• Collaborate with various teams within the company to advise on technical solutions.
• Stay informed on industry trends, best practices, and innovations, ensuring competitive market relevance of company products.

Business Data Engineer

• Hands-on Development: Actively write, review, and optimize production-grade code (PySpark, SQL) to build scalable data pipelines, data architectures, and delta tables.
• Pipeline & Architecture Management: Architect and manage robust ELT/ETL processes within Azure Data Factory and optimize cloud data structures within Azure Databricks (Delta Lake, Unity Catalog).
• BI Infrastructure: Develop and govern semantic data models, advanced DAX queries, and enterprise-grade Power BI dashboards to deliver complex insights directly to business units.
• Technical Oversight: Supervise and directly contribute to the development of AI/ML models, data pipelines, and scalable data architectures, collaborating with data scientists, engineers, and product managers.
• Shepherd Leadership: Guide SMEs and business stakeholders in identifying business needs and developing problem statements, business rules, and metrics for strategic Data & AI initiatives.
• Team & Workload Leadership: Manage a high-performing team of data engineers and analysts, balancing resource allocation, mentoring technical growth, and overseeing daily Agile delivery.
• Scope & Estimation: Define and manage the overall scope of initiatives, gain appropriate signoff from stakeholders, and work across IT and vendor partners to develop timelines and cost estimates.
• Business Case Building: Assist leadership with building business cases, tracking budget-versus-actuals, and preparing for funding approvals and various stage-gating processes.
• Lifecycle Governance: Ensure requirements are met throughout the project lifecycle process and keep stakeholders apprised of changes to scope.
• Cross-Functional Alignment: Create strong partnerships with key leaders throughout the enterprise to identify SMEs and project customers.
• Change Agent: Drive the internal adoption of modernized modern data warehouse concepts and maintain alignment between technical output and retail business goals.

Senior IT Manager, Data Engineering – Governance

• Own end-to-end architecture for the Databricks-on-Azure lakehouse platform
• Define and scale medallion architecture standards across domains
• Establish platform standards and reusable patterns to accelerate Databricks adoption
• Design and implement secure, compliant data access frameworks
• Lead CI/CD and DevOps patterns for Databricks deployments
• Optimize performance and cost across Delta Lake pipelines
• Build reusable ingestion frameworks for structured and streaming data
• Partner with Data Governance, Security, SRE, and Legal teams to align platform design
• Provide architectural guidance on PHI/PII data handling
• Mentor engineers and architects; establish design reviews and documentation practices

Senior Data Architect, Databricks

• Design, implement, and maintain logical and physical data structures within the MIR Data Layer
• Build and evolve metadata-driven schemas supporting: Document metadata, Transaction and workflow state, Event based and audit data, Reporting and analytics use cases
• Implement data solutions that support large-scale, concurrent workloads
• Optimize schemas, indexes, and access patterns to improve throughput and latency
• Leverage Memory-Optimized Tables (In-Memory OLTP) for performance-critical paths where appropriate, under architectural guidance
• Implement and support event-driven data processing within the MIR Data Layer
• Work with SQL Service Broker queues and services, including: Asynchronous processing, Status tracking and reconciliation, Retry and error-handling patterns
• Help ensure event processing is idempotent, traceable, and operationally reliable
• Build data structures that enable enterprise reporting and analytics without impacting transactional workloads
• Support snapshotting and reporting-friendly schemas for downstream consumers
• Assist in ensuring audit data is accurate, consistent, and compliant with regulatory expectations
• Contribute to cloud-readiness efforts for the MIR Data Layer
• Support migration and proof-of-concept work targeting: Amazon RDS for SQL Server, Azure SQL Database / Azure SQL Managed Instance
• Help adapt data designs to managed-platform constraints while preserving performance and correctness
• Participate in production readiness, stability, and incident resolution for the MIR Data Layer
• Debug and resolve complex production issues, including: Performance degradation, Blocking, deadlocks, and resource contention, Event based or Service Broker processing failures
• Partner closely with DBAs, SRE, and platform teams to improve monitoring and alerting, Support HA/DR and failover scenarios, Reduce repeat data-layer incidents
• Participate in on-call rotations or escalation coverage as appropriate for a senior-level role
• Ensure new data-layer implementations meet operability and supportability standards before release

• Develop and implement scalable, secure, and interoperable data systems (including data warehouses, data lakes, and real-time processing platforms) tailored for OU Health’s enterprise data analytics platform.
• Define and maintain conceptual, logical, and physical data models to standardize data across clinical, research, and operational systems.
• Ensure models support healthcare-specific requirements (clinical, claims, provider, member, etc.).
• Design and oversee data integration processes (ETL/ELT pipelines) to enable seamless data flow between OU Health and other entities as needed.
• Work closely with clinical, research, and administrative stakeholders to gather requirements and deliver data solutions that meet departmental needs.
• Facilitate effective communication and collaboration among all parties.
• Monitor and optimize database performance, troubleshoot issues, and integrate new technologies to support evolving healthcare analytics needs.
• Implement robust security and backup procedures to protect sensitive patient information and ensure compliance with privacy laws.
• Prepare and maintain system documentation, data dictionaries, and architecture blueprints.
• Provide training and guidance to team members on data management best practices.
• Identify opportunities to enhance data infrastructure, support innovation, and enable advanced analytics for improved patient outcomes and operational efficiency.
• Work closely with clinical staff, IT professionals, data analysts, and researchers to understand data requirements, workflows, and pain points.
• Facilitate regular meetings and workshops to gather input and align data solutions with clinical and operational needs.
• Act as a liaison between technical teams and non-technical stakeholders, translating complex data concepts into actionable insights.
• Ensure that data architecture decisions reflect the priorities of clinicians, administrators, and compliance officers.
• Promote a culture of knowledge sharing by documenting data models, standards, and best practices.
• Provide training and mentorship to team members on data management, security, and governance.
• Lead collaborative efforts to troubleshoot data issues, optimize workflows, and implement new technologies.
• Encourage open communication and feedback to continuously improve data systems and processes.
• Participate in project planning and execution, ensuring that data architecture aligns with project goals, timelines, and resource constraints.
• Coordinate with project managers and other architects to deliver integrated solutions.

Enterprise Data Architect

• Works with customers, business, and engineering departments to translate business objectives into system requirements.
• Provides technical advisory services from design, development, and DevOps for implementations containing multiple integrations.
• Drafts technical engineering plans, designs, and systems integration-related documents.
• Defines estimated time frames, quality, and quantity of resources required for technology projects.
• Collaborates with the Product team for feedback on platform features and functions.
• Delivers new technical projects for expansion and upsells.

AI Data Architect

• Build and support scalable data engineering solutions using Azure Databricks, PySpark, SQL, Delta Lake, Azure Data Factory, dbt, and Unity Catalog.
• Improve metadata-driven Azure Data Factory and Databricks patterns for orchestration, configuration, monitoring, restartability, and operational support.
• Develop reusable accelerators including CI/CD templates, Databricks Asset Bundle patterns, deployment automation, environment configuration, and data product onboarding templates.
• Design, develop, and support dbt models, macros, tests, documentation, and transformation standards for governed analytical data products.
• Provide guidance on appropriate technology selection and implementation patterns across dbt, Databricks notebooks and workflows, Delta Live Tables, Spark, and Starburst/Trino.
• Support cross-domain analytics initiatives by transforming source-refined data into trusted, reusable, business-aligned derivative data products.
• Leverage Unity Catalog to establish and support governed catalogs, schemas, tables, lineage, access controls, naming standards, and certification practices.
• Support Starburst/Trino as an analytical and federated query layer for governed enterprise data consumption.
• Apply Azure DevOps, Git, CI/CD, and Infrastructure as Code (IaC) practices to create repeatable, testable, and environment-aware platform delivery processes.
• Troubleshoot and resolve production issues related to orchestration, transformations, data quality, access management, query performance, deployments, and operational workflows.
• Collaborate with data engineering, analytics, platform, governance, and business teams to establish reusable, scalable, and supportable data engineering patterns.
• Contribute to the evolution of enterprise data engineering standards, governance practices, observability capabilities, and AI-ready data product frameworks.

Senior Artificial Intelligence Data Engineer

• Lead the end-to-end design, development, deployment, and support of scalable, production-grade data integration and engineering solutions
• Create and maintain technical designs and documentation aligned with business requirements, data architecture, and enterprise standards
• Collaborate with technology leadership to evaluate and implement new tools, technologies, and best practices that improve platform performance and user experience
• Partner with Product Management and business stakeholders to deliver data solutions that meet business objectives and design requirements
• Build strong cross-functional relationships, effectively communicating technical solutions and driving stakeholder alignment
• Mentor junior engineers, guide architectural decisions, and balance technical tradeoffs to deliver reliable and intuitive data products
• Drive operational excellence through monitoring, optimization, troubleshooting, and ongoing support of data platforms and services

Lead Data Engineer

• Build, maintain, and optimize data pipelines across a variety of source systems.
• Support and improve our core data warehouse infrastructure, primarily in Snowflake, with some legacy warehouse environments such as Redshift.
• Develop and maintain transformation logic, models, and reusable data assets using tools such as dbt.
• Build new warehouse functionality, curated data models, marts, and tables that support reporting, analytics, operations, and stakeholder decision-making.
• Support BI and reporting workflows across Looker and Domo, partnering with analysts and business teams to ensure trusted, consistent metrics.
• Manage and troubleshoot existing data pipelines, jobs, connectors, data shares, SFTP connections, APIs, and native integrations.
• Write and maintain production-quality SQL, Python scripts, and transformation workflows.
• Partner with analysts and business stakeholders to understand data needs and translate them into reliable, scalable data solutions.
• Help ensure our data is accurate, timely, well-documented, and trusted by the teams that rely on it.
• Explore and adopt AI-assisted engineering tools such as Claude Code, Cursor, and other agentic AI frameworks to improve development velocity, documentation, testing, data quality, and operational efficiency.
• Support warehouse migrations, platform consolidation, and modernization efforts as the company continues to scale.
• Collaborate with cross-functional teams across marketing, sales, operations, finance, product, technology, and mortgage operations.
• Contribute to data quality monitoring, observability, governance, and process improvements.

• Design and develop AI-enabled analytics solutions supporting enterprise reporting, forecasting, operational insights, and intelligent decision-making.
• Support enterprise AI initiatives including: Generative AI integrations, AI-assisted analytics, Natural language reporting, Predictive analytics, Recommendation and insight generation capabilities.
• Develop reusable analytics frameworks and scalable data products supporting enterprise reporting and AI use cases.
• Identify opportunities to leverage AI capabilities to improve reporting development efficiency and business insight generation.
• Design and implement AI-driven solutions that proactively identify data anomalies, schema changes, integration failures, reporting inconsistencies, and data quality issues across enterprise platforms.
• Develop intelligent monitoring and alerting frameworks that detect abnormal data patterns, missing data, volume fluctuations, and KPI deviations before impacting business operations.
• Create AI-enabled solutions that proactively analyze upstream system and schema changes to identify potential downstream reporting and integration impacts.
• Collaborate with governance and architecture teams to establish standards for responsible AI usage within analytics and reporting ecosystems.

AI Data Engineer

• Lead the design and delivery of database engineering solutions
• Perform applications database administration services including SQL code review, performance analysis, and database application design
• Establish database standards for operations, upgrades, migrations and onboarding new applications
• Interface with enterprise teams for Systems Administrators, Cyber Security and Capacity Planning groups
• Write clear, concise, well organized technical documentation to maintain standards and procedures

Principal Data Engineer

• Lead a team of data engineers to build and operate scalable, reliable data pipelines and platforms
• Drive engineering excellence (code quality, CI/CD, testing, observability, incident management)
• Partner with product and analytics to prioritize and deliver high-impact data use cases
• Own production support, incident response, and continuous improvement of data systems
• Establish and scale DataOps practices across development and operations
• Collaborate across AWMT D&A to enable data products that support personalization and analytics
• Hire, develop, and grow engineering talent and future tech leads
• End-to-end delivery and operation of data pipelines and platforms supporting advice products
• Engineering quality, reliability, and standardization across your team
• Talent development for data engineers and tech leads
• Cross-team alignment with product, analytics, and ML engineering
• Improving reliability and observability of production data pipelines
• Driving adoption of consistent pipeline frameworks and engineering standards
• Enabling scalable data foundations for ML and GenAI use cases
• Strengthening team execution, prioritization, and stakeholder trust

Manager, Data Engineering

• Own the data layer of the Fabric platform
• Partner closely with AI Engineers on AI-ready data products
• Design and implement scalable data storage in OneLake
• Build and maintain data ingestion pipelines
• Manage the lifecycle of Fabric data components
• Optimize Lakehouse tables for performance
• Define and operate the on-call model for production data pipelines

Fabric Data Engineer – Workplace Engineering

• Design, build, and maintain scalable data pipelines to ingest, transform, and curate data within Adobe AEP and RTCDP.
• Develop and optimize complex SQL queries and data transformations for large-scale datasets.
• Design and maintain data models (conceptual, logical, and physical) that enable efficient analytics, segmentation, and activation use cases.
• Experience working with AWS data services such as S3, Redshift, Glue, and related tooling.
• Architect end-to-end solution designs leveraging AEP components such as XDM schemas, datasets, identities, profiles, and segmentation.
• Ensure high data quality, consistency, and governance across systems by implementing validation, monitoring, and error-handling mechanisms.
• Collaborate with business stakeholders, architects, and downstream consumers to understand requirements and deliver scalable data solutions.
• Tune performance and optimize pipelines for real-time and batch workloads within RTCDP.
• Document data architecture, data flows, and operational procedures.
• Support integrations with upstream and downstream systems including CRM, web/mobile, and other MarTech and analytics platforms.
• Demonstrated experience implementing data processing, automation, validation, and orchestration logic.
• Follow best practices for security, privacy, and compliance (PII handling, consent, and identity resolution).

Customer Data Platform Data Engineer

• Define and evolve enterprise data architecture principles, standards, and reference architectures aligned to business priorities and modern practices
• Lead the design and maintenance of enterprise conceptual, logical, and physical data structures, ensuring consistent definitions and shared semantics
• Establish domain-oriented architecture patterns for reusable, governed data products and interoperable data flows
• Develop architecture patterns supporting both batch and streaming workloads, including event-driven approaches and canonical representations
• Partner with data engineering teams to translate architecture and logical designs into scalable physical implementations on AWS cloud data platforms
• Ensure consistent data definitions, lineage, and metadata documentation across domains by enabling cataloging and lineage platforms
• Maintain a central repository of architecture artifacts, definitions, and metadata documentation
• Influence data design decisions across programs and projects through architecture reviews and standards enforcement
• Facilitate architecture and data design workshops with business and technical stakeholders
• Lead, mentor, and develop a team of data architects and specialists, ensuring consistent delivery of architecture outcomes
• Stay current on evolving architecture trends and evaluate their applicability to Deckers

Director, Data Architecture

• Function as a consultant between technology unit and business units to understand their challenges
• Ask questions and present ideas to solve data problems with code
• Begin with a set of technical specifications to code solutions to business issues
• Collaborate with the business to ensure that solutions work as intended
• Do quality testing to verify that solutions meet business objectives

• Perform Data Warehouse Architecture at an intermediate level
• Design and develop data models and ETL specifications
• Conduct data analysis and recommend solutions
• Participate in data warehouse and dashboard architecture planning
• Propose requirements, standards, and best practices for data modeling and ETL
• Conduct product and vendor research and present recommendations

IS Data Warehouse Architect II

• Serves as a lead in the analysis of business requirements associated with the design and implementation of highly complex, highly scalable enterprise data processes
• Designs and develops robust and efficient processes for extracting, loading and transforming data to meet the needs of enterprise scale projects
• Provides guidance and participates in activities involved in the analysis, design, development, troubleshooting, debugging and maintenance of an enterprise data management and data ecosystem
• Identifies, investigates, tracks, and resolves the most complex technical errors ensuring the solution is timely and of acceptable quality
• Establishes and enforces database standards, guidelines, procedures and quality assurance for database deliverables such as conceptual design, logical data model capacity planning, external data interface specs, data loading, data maintenance plan and security
• Reviews and analyzes data objects and models to improve data management and data access
• Designs, constructs, tests and implements extraction and transformation programs, processes and cycles for historical and current source data
• Creates technical specification for supporting enterprise data transformation processes and supplementing system documentation for ETL processes
• Performs data profiling, source-target mappings, data transformation development, optimization, testing and implementation
• This position participates and serves as a lead in maintaining the ETL environment for the company’s system-wide enterprise data warehouse.

Supply Chain Data Engineer

• Lead architecture and design activities for data, analytics, and AI initiatives
• Define end-to-end data platform architectures, including ingestion, storage, transformation, governance, and consumption layers
• Develop target-state architectures, migration strategies, and modernization roadmaps
• Conduct architecture assessments and provide recommendations aligned to business objectives
• Collaborate with engineering teams to ensure successful implementation of architectural designs
• Establish architecture standards, best practices, and reusable patterns
• Design and implement modern cloud-based data architectures
• Lead architecture efforts involving Snowflake, Databricks, and major cloud platforms (AWS, Azure, and/or Google Cloud)
• Architect solutions leveraging data lakes, lakehouses, data warehouses, streaming platforms, and AI/ML services
• Serve as a trusted advisor to client stakeholders, including technical leaders, architects, and business executives
• Facilitate architecture workshops, discovery sessions, and strategy discussions
• Translate business requirements into actionable technical architectures and implementation plans
• Present architectural recommendations and executive-level findings to client audiences
• Support sales pursuits through solution design, proposal development, and client presentations
• Mentor architects and engineers across the organization

See all jobs on JobTailor

Senior Data Engineer

Business Data Engineer

Senior IT Manager, Data Engineering – Governance

Senior Data Architect, Databricks

Senior Data Engineer

Enterprise Data Architect

AI Data Architect

Senior Artificial Intelligence Data Engineer

Lead Data Engineer

Senior Data Engineer

AI Data Engineer

Principal Data Engineer

Manager, Data Engineering

Fabric Data Engineer – Workplace Engineering

Customer Data Platform Data Engineer

Director, Data Architecture

Data Engineer

IS Data Warehouse Architect II

Supply Chain Data Engineer

Data Architect