Data Engineer

• 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

• Understand the varying needs of nonprofits, and how to migrate associated fundraising data to Virtuous in line with best practices
• Get excited about mapping, joining, transforming and cleaning large, complex data sets using SQL
• Feel comfortable adapting customer needs on the spot to determine the best method of aligning their legacy data with the most aligned spot in our CRM
• Collaborate with our internal team on data migration strategy, tooling, and questions

Data Migration 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

• 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

• Design, build, and maintain saleable data pipelines within Snowflake from multiple sources
• Build and maintain medallion architecture (bronze/silver/gold layers) for data lake solutions
• Design data models that support both businesses intelligence and AI requirements
• Lead cloud-native data platform evolution, integrating Snowflake with AWS.
• Collaborate with business analysts, data scientists, and stakeholders to understand data requirements and translate them into technical specifications.
• Implement data quality frameworks and monitoring systems to ensure accuracy, completeness, and consistency of data assets.
• Develop and maintain comprehensive documentation for data pipelines, models, and processes.
• Optimize query performance and resource utilization to reduce costs and improve processing times.
• Participate in on-call rotation to support production data systems and resolve incidents.

• Design and develop data serving and semantic layers to support business needs
• Develop data models for use by analysts and applications using modern dimensional modeling practices
• Build and maintain robust, scalable data pipelines using ETL, ELT processes
• Create efficient and performant data models to support business requirements
• Implement and automate data quality checks and monitoring to ensure data accuracy
• Manage, troubleshoot and optimize existing data infrastructure, including cloud-based solutions
• Leverage cloud data platforms (Fabric, Databricks, Snowflake, etc.) for data storage, processing, and analysis
• Collaborate with data analysts, data scientists, and business stakeholders to understand their data needs

• Join Intel's Corporate Planning Capabilities team in supporting the biggest transformation in our company's history.
• Play a pivotal role in shaping the way SAP product master data is organized, managed, and utilized across the enterprise.
• Be the trusted subject matter expert, driving innovation and excellence in SAP master data architecture and analytics.
• Empower Intel to make informed, data-driven decisions, enabling the company to remain at the forefront of technological advancement.
• Lead transformative projects that enhance data efficiency, scalability, and compliance, contributing to our mission of delivering world-class solutions.

SAP Master Data Architect

• Manage a team by hiring and onboarding talent, setting clear expectations, conducting formal performance reviews, and coaching accountability.
• Lead the team to deliver on the enterprise data platform roadmap by translating architecture direction into sequenced execution plans, team backlogs, and measurable outcomes.
• Create a strong engineering culture across the team by setting standards for maintainability, organization, and repeatability; growing engineers via regular 1:1s, timely feedback, development plans, and formal review cycles; and being responsible for overall team performance and cohesion.
• Own day-to-day execution for the Data Engineering team by breaking work into tasks, driving sprint-level planning, delegating effectively to grow ownership, unblocking delivery, and ensuring high-quality outcomes through engineering reviews and feedback.
• Drive implementation of the Medallion architecture (Bronze → Silver → Gold) with strong enforcement of layer responsibilities, ensuring repeatable and auditable pipeline behavior leveraging Boomi, Dbt, Azure Data Bricks, Boomi/Rivery, Delta tables, and Sql Server.
• Partner closely with the Data Architect to bring the architecture to life through concrete implementation patterns, reference pipelines, and guardrails—ensuring the team can consistently execute the intended approach.
• Reduce friction by driving crisp decisions and forward motion—using lightweight decision records, seeking the right input, and then executing accountability (while avoiding overly heavy process).
• Establish DataOps practices aligned with modern software development lifecycle (SDLC): CI/CD for data assets, consistent branching/release patterns, code review standards, and runbooks/operational readiness for pipelines.
• Improve pipeline observability and operational reliability by implementing monitoring for freshness/staleness, failure modes, and quality signals, and ensuring the team responds predictably to incidents and support requests.
• Drive stakeholder partnership and intake stream: collaborate with business partners to clarify requirements, reduce ad-hoc thrash, and shape requests into deliverable, well-scoped work that fits the platform strategy.
• Develop future leaders and succession plans for key roles through assigning regularly occurring responsibilities, mentoring, and strategic delegation.
• Support and promote the company values through positive interactions with both internal and external stakeholders on a regular basis.
• Other strategic business initiatives or special cross-functional project involvement as required.

Manager, Data Engineering

• Build, mentor, and empower a high-performing team of data engineers and data professionals, fostering a culture of collaboration, innovation, and continuous growth.
• Lead the design and delivery of modern, scalable data platforms aligned with organizational goals and long-term vision.
• Guide cloud adoption and legacy system migration, including Snowflake (or similar) implementation, ensuring minimal disruption and strong data integrity.
• Oversee the design and operation of batch and real-time data pipelines using modern orchestration and engineering best practices.
• Lead integration across data lakes, warehouses, and access layers to enable seamless data consumption for analytics and business use cases.
• Ensure strong data governance, metadata management, and adherence to security, privacy, and compliance standards.
• Drive adoption of modern engineering practices, including CI/CD, infrastructure as code, and containerization (Docker/Kubernetes).
• Translate leadership vision into actionable plans and deliver measurable outcomes against key business objectives.
• Partner with Data Science, Analytics, and business teams to align priorities and unlock the full value of data.
• Define and implement frameworks, standards, and processes that improve efficiency, scalability, and reliability.
• Ensure all work meets BECU’s compliance, regulatory, and information protection requirements.
• Lead hiring, coaching, and career development efforts to grow strong and future-ready data organization.

Director, Data Engineering

• Support the development, modeling, optimization, and governance of data processes
• Design and maintain data structures that power application and operational workflows reliably and at scale
• Build and operate pipelines that move data between internal systems and key vendor integrations
• Develop consumer-ready datasets in Snowflake using best practices
• Monitor and tune Snowflake performance and pipeline reliability

• Collaborate with business users to identify, analyze, and document business requirements and key performance indicators (KPIs)
• Design, develop, and maintain data integration and automation using Power BI, Azure Databricks, Power Automate, and SmartSheets
• Collect and transform data from multiple sources into a structured format for analysis
• Develop and maintain data models and data sets for efficient and accurate reporting
• Create Power BI dataflows and shared semantic models to consolidate data model definitions
• Ensure data accuracy, integrity, and consistency within the BI solutions
• Provide training and support to Power BI developers to optimize data models in Power BI for their development needs
• Collaborate with IT teams to ensure data security, integrity, and compliance with data governance policies
• Stay up to date with the latest trends in business intelligence and analytics tools, techniques, and best practices

Senior Data Engineer

• Build and maintain high-performance pipelines using Airflow, dbt, and Python to organize raw data for analysis, modeling, and product use cases
• Partner with Product, Data Science, and ML Engineering to build the infrastructure supporting machine learning workloads, including training datasets, feature pipelines, and model output tables
• Write production-grade SQL (Amazon Redshift) and manage query optimization to support rapidly growing business needs
• Build instrumentation, monitoring, and alerting to guarantee data freshness, quality, and availability in production environments
• Identify and implement improvements by automating manual tasks and reducing pipeline runtimes
• Utilize AI-assisted developer tools (like Claude Code) daily to accelerate development and participate in design reviews and on-call rotations

• The Senior Lead Product Manager is responsible for defining and delivering the strategy, roadmap, and platform capabilities for the Zelle Data Platform.
• The role will gather and structure Zelle data assets in a way that supports analytics, near-real-time use cases, and responsible data activation across the enterprise.
• This leader will align engineering, data, risk, growth product, and enterprise data stakeholders around reusable data capabilities that create value for internal teams, clients, and the broader Zelle business.
• Own the product strategy and roadmap for the Zelle Data Platform, including capabilities that support analytics, near-real-time use cases, and responsible data activation.
• Lead the roadmap for Zelle Data Lake and the integration framework with our enterprise data platform, including delivery of the first version of the Zelle Data Lake.
• Identify, prioritize, and develop new Zelle data assets and interfaces that enable ongoing data asset growth and reuse across the business.
• Collaborate with platform consumers, including developers, data engineers, growth product teams, enterprise data partners, fraud risk management, and clients who need to leverage the platform.
• Establish product direction for data ownership, governance, availability, and quality for Zelle within the enterprise data strategy.
• Translate business, client, risk, and technical needs into clear requirements, user stories, prioritization decisions, and release plans.
• Make principled trade-off decisions between reusable platform capabilities and one-off data solutions, with clear ownership for follow-up and outcomes.
• Define and track platform success metrics such as data asset adoption, availability, quality, latency, consumer onboarding, and business value enabled.

Senior Lead Product Manager – Data Platform

• Design, develop, and maintain scalable ETL/ELT pipelines for high-volume batch and real-time data
• Build and optimize analytics infrastructure that supports developer productivity and engineering metrics
• Partner with cross-functional teams to deliver data products and self-service insights
• Develop and maintain data models, schemas, and architectures for modern data platforms
• Ensure reliability, performance, and data quality across pipelines and platforms
• Contribute to DevOps and CI/CD practices for data and backend services
• Document designs, workflows, and best practices to support team knowledge sharing

• Deploy new data pipelines
• Design & build data observability platforms and metrics
• Build metadata driven pipeline solutions
• Monitor and optimize data transformations
• Build third party data integrations

• Build Pipelines: Design and maintain scalable data pipelines to ingest and enrich healthcare data using Databricks and Spark.
• Optimize Data Workflows: Improve data intake processes and optimize SparkSQL/Python workloads for performance, scalability, and cost efficiency.
• Design Data Models: Partner with senior engineers to develop data marts and semantic models that power analytics products and reporting.
• Ensure Quality: Monitor pipeline health, troubleshoot failures, and implement data validation and quality controls.
• Learn & Grow: Expand your knowledge of the full data lifecycle, cloud infrastructure (Azure/AWS), and healthcare data standards.

Data Engineer – Entry Level

• Design, build, and maintain scalable Snowflake-based data warehouse architectures, ensuring performance, reliability, and extensibility for future growth.
• Own data architecture and technical design decisions, including data models, integration patterns, and optimization strategies.
• Transform raw source data from multiple internal and external systems into analytics-ready data models using industry best practices.
• Lead and contribute to warehouse development using dimensional modeling techniques (e.g., star schemas, slowly changing dimensions).
• Develop and manage data pipelines using Snowflake features such as Snowpipe, tasks, stored procedures, and performance optimization techniques.
• Manage and monitor data integrations with internal systems via APIs, application calls, or custom code to ensure data accuracy and reliability.
• Apply auditing, change tracking, error handling, and monitoring across large datasets to maintain high data quality standards.
• Identify and resolve data quality issues and implement controls to distinguish valid vs. invalid data used in decision-making.
• Leverage Python and application knowledge to interact with and extract data from other systems.
• Provide technical leadership through coaching and mentoring junior and mid-level data engineers, including code reviews, design feedback, and best-practice guidance.
• Contribute to and help define data engineering standards, best practices, and documentation across the team.
• Collaborate across teams to support day-to-day operational data needs and continuously improve data pipelines and processes.

• Own End-to-End Delivery of Core Data Platform Components
• Design and ship the data normalization, schema mapping, validation, enrichment, and distribution pipeline for a net-new intelligent data warehouse
• Write production code as a hands-on individual contributor. This is not a role that delegates implementation to others
• Take technical ownership from architecture through deployment, with accountability for reliability, performance, and correctness
• Drive Technical Architecture
• Partner with a small seed team of senior engineers to define the end-to-end architecture for an AI-native data warehouse serving institutional financial clients
• Bring opinionated decisions on schema design, normalization strategies, API exposure patterns, and data distribution approaches
• Evaluate and select technologies with a bias toward what ships well and scales sustainably
• Build AI Evaluation Infrastructure
• Design and implement the evaluation framework that makes AI-generated outputs trustworthy in high-stakes financial data contexts
• Build cross-model comparison tooling, deterministic validation checks, and human-in-the-loop review workflows
• Contribute to shared AI infrastructure that will serve as a foundation for multiple products across the company
• Ship with AI-Native Development Practices
• Use agentic coding tools and LLM-assisted development as your primary workflow; this is how the entire team operates
• Bring strong opinions about how to get the most out of AI-assisted development while maintaining quality and reliability
• Contribute to the team’s evolving practices around AI-accelerated SDLC
• Establish Technical Standards
• As an early senior voice on a new team, set coding standards, review practices, and architectural documentation that will scale
• Help define what “good” looks like for a team building at speed without sacrificing quality
• Mentor and provide technical guidance as the team grows from seed squad to full delivery organization
• Collaborate Across Engineering and Product
• Work closely with your engineering manager, peer engineers, and product management to translate requirements into executable technical plans
• Participate actively in design reviews and roadmap discussions with grounded, implementation-level perspective
• Engage directly with the nuances of private markets data – fund administration, investment data schemas, institutional reporting workflows – to build domain intuition that improves your technical decisions

Staff Software Engineer, Data Platform

• CSAA Insurance Group is seeking a Principal Architect to serve as our Enterprise Data Architect
• Supporting the design and delivery of secure, scalable data and AI platforms across the enterprise
• Facilitating designs and building solutions across the enterprise
• Solutions are built in a highly collaborative environment, partnering with engineering, product, and program leaders
• Leads and provides direction to Application Integration, Data, and Platform architecture vision for the enterprise
• Integrates a broad range of ideas regarding technical and solution architecture
• Creates, guides, and partners with other architects in creating and maintaining roadmaps that connect business strategies to capabilities
• Enables AI/ML use cases by supporting data pipelines, feature engineering, and model integration
• Balances speed and control to support business innovation

Enterprise Data Architect

• Define and evolve CFC-wide data architecture across all major systems and platforms
• Establish clear data domains, system boundaries, and integration patterns
• Assess current and future organizational data needs and translate them into architectural direction
• Develop long-term data architecture patterns that balance scalability, maintainability, and delivery constraints
• Design and maintain conceptual, logical, and physical data models representing CFC’s core entities, relationships, and measures
• Define, document, and govern source system schemas including canonical fields, data types, naming standards, and versioning approaches
• Establish systems of record and systems of reference for key domains such as accounts, licenses, users, and programs
• Ensure consistent implementation of data models across operational systems
• Create clear documentation that enables shared understanding across technical and non-technical stakeholders
• Architect and guide data flows between CFC systems to support operations, analytics, research, and decision-making
• Design integration and transformation approaches that preserve semantic meaning
• Oversee large-scale data integration, transformation, and storage patterns for reliable analytics
• Monitor and evolve data flows to reduce duplication, fragility, and unnecessary complexity
• Embed privacy, security, and compliance requirements directly into data models and integrations
• Partner with IT, security, and legal teams to align architecture with regulatory and policy requirements
• Ensure privacy and security protocols, including field-level encryption, role-based access control, deletion, and auditing are architected into and consistently supported across CFC’s data architecture
• Define data quality standards and validation strategies, including management of schema drift over time
• Enablement & Collaboration
• Work closely with engineers, analysts, researchers, data stewards, and business partners
• Serve as a subject-matter expert on CFC’s data architecture
• Communicate complex data concepts clearly to diverse audiences

See all jobs on JobTailor

Fabric Data Engineer – Workplace Engineering

Data Migration Engineer

Director, Data Architecture

Supply Chain Data Engineer

Data Engineer

Data Engineer

SAP Master Data Architect

Manager, Data Engineering

Director, Data Engineering

Data Engineer

Senior Data Engineer

Data Engineer

Senior Lead Product Manager – Data Platform

Senior Data Engineer

Data Engineer

Data Engineer – Entry Level

Senior Data Engineer

Staff Software Engineer, Data Platform

Enterprise Data Architect

Principal Data Architect