Analytics Engineer

• Support the U.S. Customs and Border Protection’s Power Platform team by developing, maintaining, and enhancing Power BI dashboards
• Improve the structure of Excel, SharePoint, and SQL databases that feed into the Power BI dashboards
• Create ETL processes to facilitate the movement of data from legacy databases to modernized databases
• Engage with 10,000+ users annually with the team’s current Power Platform solutions

Data Analytics Developer

• Influence technical strategy: Define and drive the long-term technical roadmap for Affirm’s Lakehouse Platform across Apache Iceberg, Spark, Snowflake, and cloud-native storage, balancing scalability, reliability, governance, performance, and cost.
• Design and develop: Architect and implement platform capabilities that make analytical data secure, trustworthy, discoverable, and easy to use across Affirm’s engineering, analytics, machine learning, and business teams.
• Strengthen governance and access controls: Design and operate secure, auditable data access capabilities across Snowflake and the lakehouse platform, including RBAC, dynamic data masking, cataloging, lineage, classification, and privacy policy enforcement.
• Improve analytics engineering foundations: Partner with Analytics Engineering to evolve data modeling, transformation pipelines, testing frameworks, documentation standards, and data quality practices that enable trustworthy self-service analytics.
• Operate at scale: Establish best practices for lakehouse operations, including schema evolution, table maintenance, partitioning, compaction, observability, incident response, production support, and readiness for on-call operations.
• Optimize performance and cost: Identify and execute improvements across analytical compute and storage, including Snowflake warehouse tuning, query optimization, storage layout, lifecycle management, cost attribution, and operational efficiency.
• Collaborate cross-functionally: Partner with Infrastructure, Lakehouse Analytics, Analytics Engineering, Machine Learning, BI, Product Engineering, and SRE to translate stakeholder needs into durable platform architecture.
• Innovate: Stay ahead of industry trends in lakehouse architecture, open table formats, analytical compute engines, data governance, privacy engineering, semantic layers, agentic data tools, and AI-ready data infrastructure.
• Build teams: Mentor engineers, raise technical quality, and foster an inclusive culture of design rigor, operational excellence, and continuous learning.

Staff Software Engineer, Backend – Lake Analytics Platform

• Consult with teams across the organization to translate key product and business processes into technical data requirements. Use dbt, BigQuery, and SQL to build up Packback’s metrics layer to enable self-serve access to core analytics.
• Build, optimize, and orchestrate data pipelines with dbt, Airflow, and Python
• Establish data integrity standards and SLAs to ensure timely, accurate delivery of data
• Build insightful and reliable dashboards to track performance of core company metrics across the product and business
• Collaborate with business operations teams to define and train on best practices with respect to data modeling, analytics, and visualization
• Collaborate with leadership to conduct analyses and apply statistical methods to answer day-to-day questions or support larger initiatives
• Work within Packback’s research function with partner institutions and perform in-depth analyses to understand how Packback’s products are driving key student outcomes
• Consult on the adoption of new technologies that impact Packback’s data infrastructure

• Build and operate core platform capabilities: Design and implement platform features that enable engineers across Affirm to build, deploy, and operate data applications at scale — covering automated provisioning, deploy pipelines, access control, service lifecycle management, and reliability tooling.
• Develop integrations: Build and maintain integrations between the platform and internal systems — CI/CD pipelines, identity and secrets management, external APIs, and event-driven hooks — so that application teams have secure, reliable, and self-service access to the tools they need.
• Strengthen data access and governance: Design and operate secure data access patterns across the platform's analytical infrastructure, including RBAC, managed-access schemas, dynamic data masking, secure views, and cross-database grants that make platform data trustworthy without creating operational bottlenecks.
• Automate toil and unlock self-service: Identify recurring support patterns — access provisioning, data pipeline failures, service health management, secrets management — and build the automation and tooling to make them self-service for platform users, eliminating manual admin work.
• Improve observability and incident response: Build telemetry, alerting, and log infrastructure so platform teams and application owners can diagnose and resolve issues independently. Establish runbooks and operational patterns that reduce blast radius and recovery time.
• Support platform operations: Participate in on-call coverage, triage production incidents across platform services and the applications that run on them, and execute platform-level maintenance with care for the engineers and workflows that depend on the platform.
• Contribute to the emerging platform roadmap: Help design and build next-generation platform capabilities — semantic layer infrastructure, agentic application patterns, and AI-native data tooling — as the platform grows to support Affirm's most ambitious data use cases.
• Raise the engineering bar: Set and hold high standards in code review, design, operational readiness, and incident follow-through. Help establish patterns that other contributors can follow and build on.

Senior Software Engineer, Backend – Lake Analytics Platform

• Developing novel, parallel algorithms to accelerate core problems in data processing and power the next generation of data center workflows
• Implementing solutions in C++ and CUDA
• Contributing to open source projects, such as cuDF, Velox, Presto and Spark
• Benchmarking, profiling, and optimizing code
• Working closely with the world’s top experts in GPU hardware, software and workflows

Senior Software Engineer, C++, CUDA – Analytics, Data Intelligence

• Partner with engineering, operations, program management, and business stakeholders to understand objectives, define reporting requirements, and deliver analytics solutions that support informed decision-making.
• Analyze large, complex datasets to identify trends, anomalies, performance gaps, risks, and opportunities for operational improvement.
• Conduct data quality assessments, validation, and verification activities to ensure accuracy, completeness, consistency, and integrity across multiple data sources.
• Design, implement, automate, and maintain scalable data collection, transformation, validation, and reporting processes.
• Develop and maintain dashboards, KPI scorecards, reports, and self-service analytics solutions that provide visibility into operational performance, program health, technical metrics, and business outcomes.
• Build data validation frameworks, monitoring processes, and quality controls to proactively identify and resolve data issues.
• Collaborate with technical teams to enhance data models, reporting structures, metadata management practices, and overall data governance initiatives.
• Translate complex technical findings into clear, concise, and actionable recommendations for both technical and executive audiences.
• Drive process improvements through automation, standardization, and implementation of scalable analytics and reporting solutions.
• Support root cause investigations by leveraging data to identify drivers of operational issues, risks, and performance challenges.
• Document methodologies, business requirements, reporting processes, and data quality standards to ensure consistency and scalability.

Data Engineer – Data Analytics, Business Intelligence

• Design, develop, and maintain scalable data pipelines and ETL/ELT workflows to support humanitarian operational and analytical needs.
• Build and optimize data models, schemas, and cloud-based data architectures to support dashboards, machine learning workflows, and advanced analytics.
• Respond quickly to database problems that arise and carry out periodic maintenance and troubleshooting.
• Monitor the system performance by performing regular tests, troubleshooting, and integrating new features.
• Integrate heterogeneous humanitarian data sources.
• Implement data quality, validation, and monitoring processes to ensure accuracy, reliability, and transparency.
• Maintain an awareness of trends and developments in database maintenance.
• Maintain documentation, metadata, and data governance standards in alignment with state department policies.
• Occasionally, respond quickly to data needs for responses to rapid onset disasters or urgent decision-making requests.
• Explore and analyze complex humanitarian datasets to identify trends, anomalies, risks, and opportunities.
• Produce actionable insights that inform policy, resource allocation, program design, and crisis response.
• Support predictive analytics or light machine-learning workflows where appropriate.
• Communicate analytical findings clearly to technical and non-technical audiences.
• Serve as a subject-matter resource for data best practices, including how to access, interpret, and apply available datasets.
• Work closely with the team business analyst and HA decision makers to understand their data needs and translate them into analytical or technical solutions.
• Provide training, informal guidance, and practical tools to staff who rely on data for decision-making.
• Demonstrate flexibility by taking on adjacent responsibilities as needed in a small, fast moving team environment to ensure project goals are met.
• Engage in continuous learning about the humanitarian sector.

Data Engineer, Analyst

• Design, develop, and maintain LookML models, views, explores, and Looker dashboards to client coding and quality standards
• Architect scalable LookML structures that support cross-domain reporting across multiple business verticals
• Conduct code reviews for offshore developers; enforce standards and best practices before client-facing delivery
• Optimize Looker report and query performance — identify bottlenecks, tune PDTs, and improve dashboard load times
• Lead migration of legacy reports to Looker; own requirements analysis, LookML build, UAT support, and sign-off
• Partner with offshore Analysts to distribute and track migration workload against sprint deadlines
• Validate migrated reports against source data and business logic; document deviations and business owner approvals
• Maintain migration tracker, flag blockers, and provide weekly status to PM
• Work hands-on with client data — manage large numbers of data sources, assess data quality, and proactively partner with client IT/data engineering teams to resolve issues
• Translate complex data models and technical constraints into clear, understandable language for business stakeholders and presentations
• Support data governance, security, and access control implementation within Looker
• Collaborate with client analytics engineers on BigQuery data model design and optimization
• Serve as the primary Looker standards resource for the EXL offshore team — build internal documentation, review checklists, and training materials
• Support onboarding of new Looker developers into client coding standards and development workflows
• Contribute to EXL's internal Looker practice — reusable patterns, accelerators, and peer knowledge transfer

Senior Looker Developer – Analytics Migration, Delivery

• Serve as a senior technical and analytical resource for Brown Advisory’s Addepar data ecosystem.
• Support, enhance, and scale Addepar workflows across portfolio management, client reporting, investment operations, performance analytics, and advisory processes.
• Analyze, troubleshoot, and resolve complex data issues involving accounts, entities, ownership structures, portfolios, holdings, transactions, securities, classifications, attributes, benchmarks, performance, and reporting outputs.
• Build, maintain, and improve data pipelines between Addepar and enterprise platforms, including Snowflake, reporting tools, client platforms, portfolio systems, accounting systems, Canoe, and other vendor applications.
• Use Addepar APIs, extracts, imports, and platform tools to enable data integration, automation, enrichment, validation, and downstream consumption.
• Develop SQL queries, reconciliation logic, exception reports, data profiling routines, and automated data quality controls.
• Partner with business and technology stakeholders to translate portfolio management, client reporting, operations, and analytics needs into data requirements, technical specifications, and scalable delivery solutions.
• Support ingestion, transformation, normalization, and validation of portfolio, client, account, security, transaction, performance, benchmark, alternative investment, and reference data.
• Contribute to Snowflake-centered data architecture by supporting secure data ingestion, modeling, quality checks, consumption-layer design, and integration with analytics and reporting platforms.
• Collaborate with investment operations and reporting teams to validate performance, exposure, allocation, classification, and client reporting data.
• Support production data operations, including data-feed monitoring, exception management, issue triage, root-cause analysis, release validation, vendor escalation, and post-incident remediation.
• Identify opportunities to automate manual workflows, reduce recurring data exceptions, improve data quality, and strengthen operational controls.
• Maintain clear documentation for data flows, mappings, business rules, reconciliation procedures, platform processes, and production support runbooks.
• Participate in platform enhancements, regression testing, user acceptance testing, release planning, and change-management activities.
• Ensure Addepar-related data processes align with Brown Advisory’s standards for security, privacy, auditability, operational resilience, and financial services regulatory expectations.
• Partner with architects, engineers, platform owners, business analysts, vendors, and data governance stakeholders to continuously improve the reliability, scalability, and usability of the Addepar data environment.

Senior Data Engineer, Analyst

• Design and develop end-to-end solutions using SAP Analytics Cloud platform, including data modeling, and visualization (knowledge of planning, predictive analytics, and collaborative features is a significant advantage)
• Design and implement data models and connections within SAP Analytics Cloud (SAC), ensuring data integrity, performance, and security
• Perform testing, debugging, and optimization of SAC applications to ensure quality, performance, and usability standards are met
• Design of new reports, interactive Business Intelligence artifacts, or data visualizations
• Refinement of data analysis work products developed by other HHS personnel
• Migration of data analysis work products from one reporting software tool to another, such as from Crystal Reports to Business Objects
• Development of new, or modification of existing, metadata describing data structures and encodings to facilitate end-user discoverability and understanding
• Development of new, or modification of existing, aggregations to facilitate analytic performance improvements
• Definition of new, or modification of existing, roles controlling access to data analysis work products
• Develop, update, integrate APIs/Restful APIs as deliverables for enterprise initiatives and projects
• Packaging of data analysis work products for deployment to test and production environments
• Analysis and resolution of defects identified in data analysis work products
• Create or modify documentation related to data analysis work products
• Create or modify training material related to data analysis work products

SAP BOBJ Analytics Developer

• Design and implement a formal business data layer in dbt, ensuring that data from multiple sources is centralized, reconciled, and serves as a single source of truth for GTM metrics.
• Collaborate with stakeholders within the GTM Operations team and Finance to define, document, and maintain alignment on key business metrics and data definitions.
• Develop and maintain automated data reconciliation and quality checks to proactively identify and resolve discrepancies, ensuring accuracy and consistency of critical reports and dashboards.
• Mentor and guide members of the data science and analytics team, establishing and enforcing best practices around data modeling, testing, documentation, and code review.
• Lead investigations into complex data anomalies, conduct root cause analysis, and communicate findings and solutions effectively to both technical and non-technical audiences.

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