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, build and maintain data systems and pipelines
• Ensure data is accessed and utilized optimally for analytics or visualization/insight generation
• Build and execute data engineering approaches to support business priorities
• Integrate varying sizes of data from different sources (including Snowflake, Fineos, Web API and Teradata)
• Create data assets that conform to scalability, extensibility, performance and maintainability requirements for the problem at hand
• Understand and contribute to the evolution of the enterprise data architecture
• Support integration of solutions within existing business processes using automation techniques
• Provide support to lower level Data Engineer peers

Data Engineer I

• 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

• 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

• 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 and build scalable data pipelines using Python (PySpark, Pandas, Airflow)
• Architect data lakes / warehouses (Snowflake, Redshift, or similar)
• Implement real-time and batch data processing frameworks
• Optimize data models for analytics, reporting, and ML use cases
• Work with datasets across portfolio & asset management, trade lifecycle & transaction data, client onboarding & KYC, performance reporting & attribution
• Build systems supporting investment analytics, risk, and regulatory reporting
• Lead a team of data engineers; provide technical guidance and code reviews
• Define data engineering best practices, governance, and standards
• Collaborate with Product, Analytics, and Business teams
• Drive data quality, lineage, and observability frameworks
• Develop solutions on AWS: S3, Glue, Lambda, EMR, Redshift
• Implement CI/CD pipelines and infrastructure-as-code (Terraform/CloudFormation)
• Ensure security and compliance (FINRA, SEC considerations)

Data Architect

• 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

• Serve as the expert for ETL and DB solutions, collaborating with business stakeholders and IT teams to define requirements, gather data, and implement optimized data solutions.
• Design, implement, and maintain data systems in Snowflake to ensure data scalability and accessibility.
• Implement and manage data lakes and data warehouses, creating pipelines and data models to enable efficient analytics and reporting.
• Establish and document strategies for managing data transfer processes, including secure file transfers (SFTP), batch data processing, and real-time streaming.
• Build and optimize ETL pipelines for data extraction, transformation, and loading into operational databases or analytical platforms.
• Integrate and support data visualization tools such as Power BI, Sisense, Google Looker, Tableau, or similar platforms to enable actionable insights for business stakeholders.
• Develop and maintain optimized data models for dashboards and reporting, ensuring compatibility with visualization tools.
• Plan, coordinate, and implement database migrations, upgrades, and patches with minimal downtime.
• Define and enforce database governance policies, including data integrity, security, and compliance with regulatory requirements.
• Analyze and resolve database performance issues by optimizing queries, indexes, and schema designs.
• Partner with vendors to evaluate, select, and implement database tools, services, and technologies; stay informed about product roadmaps and industry trends.
• Develop disaster recovery and high-availability solutions, including replication, clustering, and failover.

• 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

• 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

• Joining each and every one of your colleagues in creating an inclusive workspace;
• Owning, maintaining, and supporting the reliability, architecture, and evolution of our production machine learning and delivery platform;
• Designing, operating, and improving large scale Databricks/Spark pipelines and workflows
• Partner with our data scientists to productionize new data and ML-powered products;
• Leading improvements on the software integration layers between data pipelines and product workflows;
• Growing the team via mentorship and leadership, reviewing technical documents, improving code quality, and improving team performance and processes;
• Acting as a senior technical owner in debugging complex production issues and improving system observability;
• Collaborating with cross-functional teams to support their data infrastructure needs.

Senior Software Engineer, Data Platform

• 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, implement, and optimize ETL processes to both our Snowflake environment as well as legacy SQL Server
• Create and maintain data models and databases that support analytical and reporting needs across all lines of business
• Develop and implement validity checks to monitor and assure data products meet quality thresholds for accuracy and reliability
• Work collaboratively within cross-functional teams to gather data requirements and document business expectations and needed deliverables
• Identify and resolve inefficiencies within production data pipelines
• Produce and maintain comprehensive documentation of architectures, processes, and systems
• Adhere to all regulatory and compliance requirements

See all jobs on JobTailor

Supply Chain Data Engineer

Data Engineer I

Manager, Data Engineering

Senior Data Engineer

Data Engineer

Data Engineer

Data Architect

Senior Data Engineer

Staff Software Engineer, Data Platform

Senior Data Engineer

Enterprise Data Architect

Data Architect – Data Ecosystems

Senior Software Engineer, Data Platform

Senior Data Engineering Manager

Data Engineer