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

• 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

• Design, build, and maintain scalable ERP data pipelines for platforms such as Workday and enterprise systems
• Own end‑to‑end lifecycle of ERP data pipelines (ingestion → transformation → delivery)
• Support data conversion and migration initiatives (e.g., M&A, system integrations)
• Ensure pipeline reliability, recoverability, and stable production operations
• Troubleshoot pipeline failures and resolve root causes of data defects and operational issues
• Partner with business analysts and stakeholders to translate requirements into data solutions
• Implement and enforce SDLC best practices (version control, code reviews, CI/CD, release management)
• Develop and maintain data quality controls (freshness, completeness, anomaly detection, schema management)
• Detect and manage schema drift and downstream data issues proactively
• Build and maintain monitoring, alerting, and observability for data pipelines
• Optimize performance, scalability, and cost of data workloads
• Track operational KPIs (e.g., pipeline success rates, SLAs, incident trends, recovery times)
• Create runbooks, documentation, and audit‑ready operational processes
• Collaborate with data governance, security, and compliance teams on controls and data access
• Support analytics, reporting, and integration teams with reliable, well‑governed datasets
• Communicate effectively with technical and non‑technical stakeholders and escalate risks early

Senior ERP Data Engineer

• Evolving data infrastructure and data analytics capabilities
• Building architecturally-sound data systems and pipelines for analytics and data science
• Leading full stack development projects
• Collaborating with cross-functional teams
• Managing data logistics including specifications, transfers, and structures
• Accessing, extracting, and transforming data from various sources using tools like Hadoop, Spark, and SQL
• Supporting analytical proof of concepts and tool exploration
• Developing scalable ETL pipelines and ensuring data quality and reliability
• Assisting in Agile/Lean methodologies and DevOps practices

• 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

• Develop and execute a comprehensive data strategy, aligned with the organization’s goals, and focuses on digital engineering, digital thread, and advanced analytics across the EMS Service Line.
• Oversee the data models, aligning data models across engineering and manufacturing to support user needs and align with other service Lines and CIDO.
• Collaborate with engineering and business leaders to align data initiatives with program goals as well as enterprise data governance team.
• Develop and enable program data risks management process in concurrence with program risk management.
• Maintain knowledge of current and emerging Industry and government trends and technologies in data frameworks, management and analytics and ensure proper utilization where applicable.
• Display strong stakeholder management skills, including presenting solutions to senior leadership.
• Drive company-wide data strategy and innovation, aligning technical goals with business objectives.
• Influence and gain buy-in from key stakeholders, ensuring alignment and successful project delivery.
• Manage data governance, data security, data modeling, and enterprise architecture frameworks.

Senior Staff Systems Data Architect

• 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

• Own end‑to‑end reliability of data pipelines across ingestion, transformation, and serving layers.
• Design scalable ELT/ETL pipelines with retry, backfill, and safe reprocessing capabilities.
• Implement monitoring, alerting, and incident response patterns to improve pipeline stability.
• Enforce SDLC best practices (branching strategy, code reviews, CI/CD, release management).
• Establish traceable change management with documented approvals and rollback plans.
• Implement and maintain data quality testing, freshness checks, and anomaly detection.
• Detect and manage schema drift and breaking changes proactively.
• Improve pipeline observability through monitoring of job failures, anomalies, and cost spikes.
• Optimize pipeline performance and warehouse costs through workload tuning and partitioning.
• Track and report on reliability KPIs (e.g., pipeline success rate, SLA adherence, MTTR).
• Develop and maintain runbooks, recovery procedures, and operational documentation.
• Automate common operational workflows such as backfills and dependency management.
• Partner with Analytics Engineering and BI teams to support reliable downstream consumption.
• Collaborate with Platform, Governance, and Security teams on controls and audit readiness.
• Support delivery of incremental releases to improve stability and reduce operational risk.

Senior Data Engineer

• 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.

• Develop and execute a comprehensive data strategy, aligned with the organization’s goals, and focuses on digital engineering, digital thread, and advanced analytics across the EMS Service Line.
• Oversee the data models, aligning data models across engineering and manufacturing to support user needs and align with other service Lines and CIDO.
• Collaborate with engineering and business leaders to align data initiatives with program goals as well as enterprise data governance team.
• Develop and enable program data risks management process in concurrence with program risk management.
• Maintain knowledge of current and emerging Industry and government trends and technologies in data frameworks, management and analytics and ensure proper utilization where applicable.
• Display strong stakeholder management skills, including presenting solutions to senior leadership.
• Drive company-wide data strategy and innovation, aligning technical goals with business objectives.
• Influence and gain buy-in from key stakeholders, ensuring alignment and successful project delivery.
• Manage data governance, data security, data modeling, and enterprise architecture frameworks.
• Provide experience in understanding, managing, and integrating business or government program data across a variety of tool sets and data sources.
• Data Architecture development with an understanding of data lakes, warehouses, and/or streaming platforms.
• Continuous Integration/Continuous Deployment experience with CI/CD tools like Git tailored for data pipelines.
• Cloud Expertise experience and/or knowledge in cloud platforms like AWS or Azure.
• Support data integration strategy between pillar systems.

• Architecting and building the very foundation of Pattern's data-driven future.
• Tackling massive, petabyte-scale challenges.
• Transforming raw data into high-octane fuel for AI models and global marketplace strategies.
• Leading high-stakes technical initiatives that directly accelerate growth for global brands.

• 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

• Design, build, and deploy robust data platforms, including data lakehouses and warehouses, and automate their operation using tools like Azure DevOps, Terraform, and CloudFormation
• Implement and optimize advanced ETL/ELT data pipelines and orchestrate complex data jobs using platforms like Databricks notebooks and dbt
• Design and maintain data models, potentially utilizing data virtualization tools like Cube
• Leverage managed and serverless cloud offerings to create performant and scalable cloud-native application solutions and data pipelines
• Apply best practices for data security and ensure compliance with governance requirements
• Build high-performance data pipelines and integrate with BI tools like Tableau, Looker, and Power BI
• Provide leadership in applying software development best practices, including CI/CD and managing Infrastructure as Code

• Design the vision and blueprint of an organization’s data framework.
• Design and manage data systems, set policies for how data is stored and accessed, coordinate various data sources within an organization, and integrate new data technologies into existing IT infrastructures.
• Data modeling and design, including software development and database administration.

Data Architect – Data Ecosystems

• Define and drive the architectural strategy for migrating large-scale, on-premises data systems to AWS, including re-platforming and re-architecture efforts.
• Establish migration patterns, data residency strategies, and cutover plans that minimize risk and downtime.
• Design and evolve cloud-native data architectures that support long-term scalability and performance.
• Lead the architecture of large-scale NoSQL data systems, including Couchbase, DynamoDB, and related technologies.
• Oversee the design and governance of our Snowflake-based data platform, including data modeling, ingestion pipelines, and access patterns.
• Ensure systems are optimized for performance, reliability, and cost efficiency at scale.
• Architect and implement event-based data infrastructure using technologies such as Kafka, SQS, and SNS.
• Define standards for event schema design, topic management, consumer patterns, and data lineage.
• Enable reliable, scalable, and observable data flows across distributed systems.
• Establish and enforce data governance standards, including classification, access controls, encryption, retention, and auditability.
• Ensure alignment with regulatory frameworks such as SOC 2 Type II and HIPAA.
• Partner with Security teams on threat modeling and risk mitigation for data infrastructure.
• Lead architectural reviews, RFC processes, and documentation standards.
• Partner with engineering leadership to influence roadmap prioritization through data-driven insights.
• Represent the data platform in cross-functional planning and executive-level discussions.

Data Architect

• Lead the data organization through directors and managers, focusing on the strategic evolution of data engineering and platform architecture.
• Architect and scale distributed systems capable of processing petabyte-scale datasets and hundreds of billions of events.
• Collaborate with cross-functional teams to drive a company-wide data vision and operating model aligned with corporate goals.
• Obsess over the "developer experience," building self-service capabilities and abstractions that reduce time-to-delivery from days to minutes.
• Manage the performance and cost efficiency of the platform, including transitions toward open-source technologies like Airflow, Debezium, Iceberg, Trino, and Spark.
• Mentor international engineering teams, establishing high-level frameworks for technical excellence and operational rigor.

VP of Data Platform – Architecture

• The Data Warehouse Engineer is a hands-on role responsible for designing, building, and operating Maverick’s Azure data platform.
• Reporting to the Data Team Director, you will partner with external implementation resources in Phase 1 and then own daily operations, reliability, and continuous improvement of the platform.
• Monitor health and SLAs via Azure Monitor/Log Analytics, Synapse and Data Factory dashboards; respond to critical pipeline incidents within 2 hours.
• Triage and resolve data integration failures (sponsor bank files, CRM/ITSM connectors, API ingestions), distinguishing data quality vs. pipeline vs. infrastructure issues; escalate when needed.
• Perform root-cause analysis (RCA) and drive corrective actions; maintain and conduct post-mortems.
• Design and build Azure Data Factory pipelines (ETL/ELT) for batch and incremental ingestion using parameterized, reusable templates.

Data Warehouse Engineer

• Build and maintain ELT pipelines that ingest data from systems.
• Co-own the mapping and migration of source data into the new 3NF EDW, ensuring data integrity, reducing redundancy, and maintaining automated unit and data tests.
• Develop data observability processes and monitoring dashboards to track pipeline health, freshness, and data quality across Databricks.
• Build new data and AI-powered tooling to improve the productivity of the data engineering team and broaden self-service data access for Route employees and external partners.
• Help harden the Integration Pipeline by automating deployment of shared staging and production infrastructure for new pipelines and managing dependency updates for dbt and CI templates.
• Support the full migration from Snowflake to Databricks, targeting completion by end of Q2 2027, including reporting services and ingest/egress jobs.
• Coordinate with engineering, analytics, product, and business teams to define and prioritize data requirements and ensure end-to-end data lifecycle coverage for existing and new products.
• Champion data democratization, help establish a company-wide data retention policy, and expand the foundation for a self-service Silver layer (EDW) that serves as a single source of truth.

• Engage with multiple departments throughout the organization to take their data and transform it into information that will enable them to make better decisions.
• Gather business requirements and design, create, and support the data warehouse.
• Work with divine guidance to provide or support technology that furthers the mission of the Church.

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Director, Data Architecture

IS Data Warehouse Architect II

Supply Chain Data Engineer

Senior ERP Data Engineer

Data Engineer

Manager, Data Engineering

Senior Staff Systems Data Architect

Data Engineer – Entry Level

Senior Data Engineer

Senior Data Engineer

Senior Staff Systems Data Architect

Senior Data Engineer

Staff Software Engineer, Data Platform

Data Engineer

Data Architect – Data Ecosystems

Data Architect

VP of Data Platform – Architecture

Data Warehouse Engineer

Data Engineer

Data Engineer 1 – 5 Year Fixed Term