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

• Define and evolve enterprise data architecture principles, standards, and build specifications.
• Produce detailed technical development artifacts including data flow diagrams, process flows, and system interactions.
• Partner with collaborators to map processes, data flows, and system dependencies end-to-end and translate complex business processes into clear data and process flow builds.
• Design end-to-end data lifecycle within the enterprise data platform, from source systems through standardization to consumption.
• Develop and maintain conceptual, logical, and physical data models that align with enterprise standards.
• Establish and enforce data modeling standards, naming conventions, and documentation practices.
• Define and manage data abstraction layers (conceptual, logical, physical, semantic).
• Apply guidelines across 3NF, dimensional, and Data Vault modeling approaches.
• Ensure alignment between business definitions, data structures, and downstream consumption needs.
• Build and maintain canonical data models to enable consistency across systems and domains.
• Define enterprise integration patterns for data sharing (cloud share, batch, streaming, APIs, event-driven) and their appropriate use.
• Architect for interoperability across platforms, enabling decoupled and reusable data assets.
• Define data contracts and interface specifications between producers and consumers.
• Make and detail architecture trade-offs (e.g., normalization vs usability, latency vs consistency).
• Promote data quality, lineage, and governance throughout data flows.
• Perform data analysis and data profiling to validate assumptions and inform builds.
• Guide and mentor team members on architectural standards and technical build guidelines.

Enterprise Data Architect

• 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 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

• 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

• Define and drive the global data platform strategy ensuring aligned, scalable, secure, and cost-effective platforms
• Communicate strategy, progress, and outcomes to executive leadership
• Foster consensus and guide investment decisions focusing on value, prioritization, and cost efficiency
• Establish and implement the technical roadmap for data platform modernization, AI-ready data, and data capabilities
• Ensure data platforms are AI-ready, providing high-quality, well-structured, and discoverable data to support advanced analytics and machine learning
• Collaborate across data, technology, and business teams to provide platform capabilities and data solutions
• Establish clear operating models, roles, and decision frameworks to enable consistent execution
• Ensure high availability, performance, security, cost efficiency, and reliability of data platforms
• Drive engineering and operational excellence by establishing automated and AI-enabled practices
• Lead and develop a global organization of platform, engineering, and operations leaders and engineers

VP, Global Data Platforms

• Owns the Marketing and Revenue Management data architecture strategy and multi-year roadmap
• Modernizes platforms, establishes architectural standards, and enables scalable, cloud-native solutions
• Partners closely with executive leadership, commercial stakeholders, and technology teams
• Defines and executes a multi-year data architecture roadmap aligned to commercial strategy
• Owns architectural standards for Databricks, Delta Lake, Unity Catalog, and cloud-native data environments
• Architect Marketing and Revenue Management semantic models, KPI logic, and unified metric layers
• Leads governance frameworks across metadata, lineage, security, compliance, and regulatory standards
• Establishes data observability and quality standards using tools such as Monte Carlo, Soda, and Great Expectations
• Chairs architecture councils and mentors senior architects
• Evaluates emerging technologies and guides modernization toward scalable architectures

Principal Commercial Data Architect

• Define the multi-year architectural vision for MGM’s Lakehouse platform, including compute, storage, orchestration, semantic layers, governance, and reliability.
• Architect scalable solutions for high-volume Gaming, Hospitality, Loyalty, and Marketing datasets across real-time, batch, and federated environments.
• Establish Marketing and Revenue Management data modeling standards across fact/event layers, dimensional schemas, KPIs, and unified semantic definitions.
• Lead adoption of modern data engineering patterns such as streaming pipelines, Delta Live Tables, feature stores, and ML observability.
• Own Marketing and Revenue Management semantic models and certified curated datasets to ensure metric consistency and governance alignment.
• Define organizational standards for data quality, observability, SLAs/SLOs, and incident lifecycle management.
• Implement automated frameworks for schema drift detection, anomaly monitoring, and pipeline self-healing.
• Optimize Databricks workloads for performance and cost efficiency (ZORDER, OPTIMIZE, workload isolation).
• Mentor Senior and Staff Data Engineers and lead engineering councils.

Principal Commercial Data Engineer

• 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

• 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.

See all jobs on JobTailor

Lead Data Engineer

Enterprise Data Architect

Supply Chain Data Engineer

Data Engineer

Manager, Data Engineering

Data Engineer

Data Engineer

Senior Data Engineer

Staff Software Engineer, Data Platform

VP, Global Data Platforms

Principal Commercial Data Architect

Principal Commercial Data Engineer

Data Architect – Data Ecosystems

Data Warehouse Engineer