Data Engineer

• 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 and Develop Data Solutions
• Build, maintain, and optimize data pipelines and workflows that support reporting, analytics, and business applications using SQL, Informatica, IICS, and Snowflake.
• Data Processing and Preparation
• Develop and manage data preparation routines for extracts, transformations, and reporting datasets, ensuring reliability and performance.
• Data Modeling and Optimization
• Create and maintain logical and physical data models following normalization principles and best practices.
• Tune queries for optimal performance across Snowflake and Oracle environments.
• Integration and Automation
• Work with various structured and semi-structured data formats (XML, JSON) and leverage AWS cloud frameworks for scalable data integration and processing.
• Data Security and Quality
• Ensure compliance with data security policies and implement best practices for data protection, integrity, and confidentiality.
• Collaboration and Leadership
• Partner with data analysts, developers, and business stakeholders to define data requirements, streamline processes, and improve data accessibility for reporting tools such as Power BI.
• Continuous Improvement
• Recommend and implement improvements to data architecture, workflows, and governance processes.
• Serve as a mentor to junior team members and promote a culture of curiosity and continuous learning.

Senior Data Engineer

• Designs, develops and optimizes highly complex, specialized data warehouses
• Analyzes business intelligence data and makes recommendations for growth and integration
• Works with IT and business customers to develop requirements for data warehouse architecture
• Provides support and guidance to ensure architecture aligns with BI application needs
• Leads and consults on implementation of data warehouses and ensures data integrity

Senior Data Architect – Retail Architecture

• Architecting, creating and maintaining data pipelines
• Assist with renovating the data management infrastructure to drive automation in data integration and management
• Work in partnership with data science teams and with business analysts in refining their data requirements
• Train counterparts across the organization in data pipelining and preparation techniques
• Work with data governance teams and participate in vetting and promoting content created in the business
• Demonstrated ability to communicate complex results to technical and non-technical audiences
• Work effectively in teams as well as independently across multiple tasks
• Strategic, intellectually curious thinker with focus on outcomes

• Design, build, and deploy AI-enabled applications that support real operational workflows and decision-making for DoW, federal, and enterprise environments.
• Develop application logic that integrates AI models, APIs, data pipelines, user interfaces, and enterprise systems.
• Implement retrieval-augmented generation, semantic search, embeddings, vector databases, prompt strategies, and agent-based workflows where appropriate.
• Integrate structured and unstructured enterprise data sources into AI-enabled applications.
• Build orchestration workflows that connect foundation models, machine learning services, tools, APIs, and secure enterprise environments.
• Develop evaluation frameworks to measure AI output quality, grounding, accuracy, traceability, and failure modes.
• Implement guardrails, validation checks, fallback logic, and human-in-the-loop review points to support reliable AI behavior.
• Support training, tuning, evaluation, and validation of AI or machine learning models using enterprise data while ensuring outputs remain defensible, explainable, and aligned to approved methodologies.
• Develop secure APIs, backend services, dashboards, visualizations, and integration components for AI-enabled analytic tools and decision-support platforms.
• Implement monitoring, logging, observability, model performance tracking, and cost controls for AI systems in operation.
• Advise on model transparency, explainability, auditability, data governance, information assurance, and access controls required for secure DoW and federal deployment.
• Collaborate with functional SMEs to translate business needs, mission requirements, workflow requirements, review expectations, and operational constraints into technical requirements.
• Contribute reusable AI components, accelerators, reference architectures, and delivery patterns for future enterprise and federal AI implementations.
• Support demonstrations, pilots, proofs of value, testing, deployment readiness, and user acceptance activities with internal and client stakeholders.

AI/Data Engineer

• 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

• 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

• 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.

• 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 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

• Design and evolve an enterprise transactional data model for lending payment processing (OLTP), including schemas, constraints, indexing strategies, partitioning, and retention/archival patterns.
• Define database standards for reliability, scalability, performance, and resiliency across MySQL and Oracle.
• Implement and operate high availability and disaster recovery patterns; validate operational readiness (failover/failback, replication health, recovery objectives).
• Drive performance engineering: query tuning, index strategy, execution plan analysis, contention/locking analysis, and workload management.
• Implement automation for database operations (provisioning support, patch readiness support, backup validation, health checks, and runbook-driven execution).
• Design and support replication/data movement strategies as needed for resiliency and/or cross-site requirements (preferred Oracle skills include Golden Gate and Data Guard).

Data Engineer – Oracle, MySQL, OLTP

• Assists in developing, supporting and implementing data solutions for multiple applications
• Leverages technical knowledge and industry experience to design, build and maintain technology solutions
• Performs data requirement analysis and the data preparation process development
• Assists in designing and building data service infrastructure on multiple data platforms
• Participates the development and implementation of data solutions for multiple applications
• Consults on data migration and transformation to ensure the accuracy and security of data solutions

Data Engineer Associate – PySpark, Hadoop, SQL

• Act as an internal consultant for other data engineering and technical teams to enhance and optimize the quality of their code, ensuring adherence to engineering best practices
• Design, build and test data integration processes between business-critical applications to solve complex problems within financial services
• Define and implement internal process improvements through the development of detailed analytics that provide actionable insights into key business performance metrics
• Leads in developing, supporting and implementing data solutions for multiple applications in order to meet business objectives and user requirements
• Oversees the development and implementation of data solutions for multiple applications to ensure its scalability, availability and maintainability

Senior Data Engineer – Data Products, Cloudera Hadoop

• Lead, manage, and develop a team of data engineers, fostering a culture of technical excellence, independence, collaboration, and accountability.
• Create and execute a Data Engineering roadmap that helps ReUp to achieve its goals.
• Partner with technology, analytics, finance, B2B, and B2C teams to define data requirements, and design and develop solutions that drive value for ReUp’s clients and learners.
• Oversee the design, development, testing, implementation, maintenance, and continuous improvement of data pipelines and warehouses that ingest, transform, store, and provide access to student and institutional data to power business intelligence products, ML models, cloud-based web applications, and marketing and coaching services.
• Champion automation, observability, and performance to ensure ReUp’s pipeline efficiently and reliably delivers quality data to end users.
• Proactively identify, triage, and resolve data quality and availability issues through root-cause analysis, remediation, monitoring, and cross-functional collaboration.
• Define and monitor SLAs for data quality, workflow status and runtime, incident response and resolution time, and team velocity.
• Define and implement data engineering standards and best practices.
• Support data security, compliance, and governance initiatives.

Senior Data Engineering Manager

• Design and evolve an enterprise transactional data model for lending payment processing (OLTP), including schemas, constraints, indexing strategies, partitioning, and retention/archival patterns.
• Define database standards for reliability, scalability, performance, and resiliency across MySQL and Oracle.
• Implement and operate high availability and disaster recovery patterns; validate operational readiness (failover/failback, replication health, recovery objectives).
• Drive performance engineering: query tuning, index strategy, execution plan analysis, contention/locking analysis, and workload management.
• Implement automation for database operations (provisioning support, patch readiness support, backup validation, health checks, and runbook-driven execution).
• Design and support replication/data movement strategies as needed for resiliency and/or cross-site requirements (preferred Oracle skills include Golden Gate and Data Guard).
• Partner with security to enforce encryption-in-transit/at-rest, least privilege, auditing, secrets management integration, and vulnerability remediation for database platforms.
• Work closely with application teams on schema evolution, release coordination, and safe migration approaches (including MySQL ↔ Oracle considerations).

See all jobs on JobTailor

Supply Chain Data Engineer

Senior Data Engineer

Senior Data Architect – Retail Architecture

Senior Data Engineer

AI/Data Engineer

Senior Staff Systems Data Architect

Data Engineer

Senior Data Engineer

Senior Staff Systems Data Architect

Staff Software Engineer, Data Platform

Data Architect – Data Ecosystems

Data Engineer – Oracle, MySQL, OLTP

Data Engineer Associate – PySpark, Hadoop, SQL

Senior Data Engineer – Data Products, Cloudera Hadoop

Senior Data Engineering Manager

Senior Data Engineer – OLTP, Data Guard, Golden Gate