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

• 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

• 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 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

• 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.

Senior Data Engineer

• 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

• You will work closely with finance, accounting, billing, and revenue teams to understand their main pain points and translate them into self-serve, reliable, and scalable data solutions
• You are expected to mentor other team members and be a champion of engineering excellence across the organization
• You will be part of a small team with a large amount of ownership and autonomy, managing initiatives directly from design through execution
• You will have the freedom to suggest and drive organization-wide initiatives that shape the financial data vision and roadmap
• You will own critical data integration pipelines and models, ensuring uniform, reliable, timely, and accurate financial reporting

Senior Data Engineer, Finance

• Drive the Data Architecture and Data platform modernization for the Workplace Investing division of Fidelity Investments
• Collaborate closely with business partners and technology teams to understand business objectives
• Define strategies to modernize legacy systems
• Leverage AI/ML-enabled approaches for mainframe modernization
• Design and lead proof-of-concepts that demonstrate AI value in platform modernization

Director, AI/ML, Data Architecture

• 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

• Collaborate with data scientists and machine learning engineers to understand data requirements for LLM and machine learning model fine-tuning.
• Design, build, and maintain scalable data pipelines to ingest, process, and store massive and diverse healthcare datasets.
• Implement robust data validation and monitoring to ensure the integrity, accuracy, and consistency of all training datasets.
• Implement robust data cleaning, validation, and transformation processes to ensure data quality and integrity.
• Develop and optimize data structures and schemas for efficient access and utilization by LLMs and machine learning models.
• Work with the team to identify and acquire new data sources, ensuring compliance with relevant healthcare regulations (e.g., HIPAA).
• Monitor data pipeline performance, troubleshoot issues, and implement optimizations to improve efficiency and reliability.
• Document data engineering processes, data models, and data dictionaries.
• Stay up-to-date with the latest advancements in data engineering, big data technologies, and machine learning.

AI Data Engineer

• 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

• Provide senior-level business and systems analyst and technical expertise during all phases of data mart/business intelligence lifecycle.
• Participate in data mart and business intelligence development efforts.
• Participate in architecture of data mart/business intelligence projects.
• Work with analysts, programmers, business process owners, and end-users to determine scope and requirements for data mart/business intelligence solutions.
• Perform analysis, database and meta-data modeling, and establish logical and physical design for reporting solutions.
• Develop technical infrastructure for data mart/business intelligence initiatives.
• Develop reports and business intelligence solutions to meet key business needs for critical administrative systems.
• Provide senior level guidance to other staff with report development questions and provide mentoring on report development approaches.
• Lead mid-sized to large technical and business intelligence projects.
• Provide operational support for business intelligence solutions and respond to changes required by upgrades, regulatory requirements, etc.
• Other duties as assigned.

See all jobs on JobTailor

Principal Data Engineer

Supply Chain Data Engineer

Manager, Data Engineering

Data Engineer

Data Engineer

Senior Data Engineer

Staff Software Engineer, Data Platform

Senior Data Engineer, Finance

Director, AI/ML, Data Architecture

Data Architect – Data Ecosystems

AI Data Engineer

Senior Data Engineering Manager

Data Engineer – Workday/Fabric