Data Engineer

• Focus on the technical design, development, enhancement, testing, debugging and maintenance of Cloud applications
• Maintain applications and infrastructure for cloud-based platforms
• Support the creation of strategies to enhance the performance of existing cloud technologies
• Provides specialized consulting, analytical and technical support
• Works independently and regularly handles non-routine situations

Data Engineer, AWS

• Own the operational health, architecture intelligence, and cost optimization of cloud environments
• Develop and maintain technical understanding of the organization’s cloud architecture
• Map relationship between architectural decisions and cost drivers
• Identify cost anomalies, inefficiencies, and optimization opportunities
• Build and maintain dashboards for real-time visibility into cloud cost trends
• Help define and enforce cloud governance policies
• Serve as primary point of contact for cloud cost and operations questions

Operations Data Engineer

• Support team data science modeling and problem-solving efforts, primarily in the area of sensor data ingestion, anomaly detection, predictive maintenance tooling.
• Propose data engineering solutions to support different modeling strategies, including AI/ML and agentic systems.
• Design, build and support healthy, automated, and repeatable data ingestion and processing pipelines, including delta tables and knowledge graphs, and the implementation of MLOps pipelines.
• Monitor and maintain data quality, integrity, consistency.
• Work on systems to monitor system health, data quality and scientific performance.
• Help design and build scalable, reliable, and high-performance systems and environments.
• Take part in implementation and support of continuous integration and continuous delivery (CI/CD).
• Contribute to technical documentation.

• Design and build real-time and batch data pipelines supporting high-volume data workloads
• Develop scalable systems for data ingestion, transformation, enrichment, and delivery
• Own the lifecycle of data pipelines, including development, testing, deployment, monitoring, and optimization
• Improve data quality, observability, reliability, and operational performance across data platforms
• Optimize data processing systems for scalability, latency, and cost efficiency
• Partner with Analytics, Product, and Engineering teams to support reporting, automation, and downstream data consumption
• Lead technical discussions, design reviews, and architecture decisions related to data platforms
• Participate in production support and incident response for critical data systems

Senior Data Engineer

• Work closely with experienced engineers and analysts to build reliable data pipelines
• Write code daily, solving real data challenges
• Contribute to scalable data solutions
• Collaborate in a team culture where learning and mentoring are emphasized
• Gain hands-on experience with modern data tools and technologies
• Contribute to meaningful projects that support real business decisions

Associate Data Engineer

• Work closely with experienced engineers and analysts to build reliable data pipelines
• Shape how data flows across the organization
• Writing code daily, solving real data challenges, and contributing to scalable data solutions
• Collaborate in a team where learning and mentoring are part of the culture
• Gain hands-on experience with modern data tools and technologies
• Build a solid foundation for a long-term career in data engineering
• Contribute to meaningful projects that support real business decisions

• Help design and deliver scalable data solutions that power decision-making across the organization.
• Take ownership of key components of the data platform.
• Collaborate with teams across the business.
• Contribute to architectural decisions.
• Support the growth of other engineers through mentorship and knowledge sharing.
• Solve complex data challenges through clean, maintainable code.

• Help design and deliver scalable data solutions that power decision-making across the organization.
• Take ownership of key components of the data platform, working hands-on with modern tools and technologies to build reliable, high-quality data pipelines.
• Collaborate with teams across the business, contribute to architectural decisions, and support the growth of other engineers through mentorship and knowledge sharing.
• Solve complex data challenges through clean, maintainable code.

• Design and implement dimensional and relational data models (star/snowflake schemas) that support scalable, performant analytics across business domains.
• Develop, document, and maintain transformation logic that converts raw source data into clean, conformed, and business-ready datasets.
• Write and optimize complex SQL queries and dbt models for data manipulation, aggregation, and layered transformation within the data warehouse.
• Define and enforce data standards, naming conventions, and modeling best practices to ensure consistency across the warehouse.
• Collaborate with stakeholders to translate business logic into well-structured data models that are intuitive and reusable.
• Evaluate and refine existing models over time as business needs evolve, ensuring models remain accurate, efficient, and well-documented.
• Design, develop, and maintain Python scripts for extracting and loading data from various source systems into our data warehouse.
• Ensure data quality, accuracy, and consistency throughout the pipeline, from ingestion through to reporting-ready layers.
• Identify and resolve data pipeline issues, ensuring timely and reliable data availability.
• Contribute to the documentation of ETL processes, pipeline architecture, and data definitions.
• Develop, maintain, and optimize interactive dashboards and reports using Power BI to visualize KPIs and business trends, built on top of well-modeled data layers.
• Perform ad-hoc data analysis using SQL and collaborate with stakeholders across Marketing, Sales, Product, and Operations to translate business questions into clear, actionable insights.

• Work with large data sets and implement sophisticated data pipelines with both structured and semi-structured data
• Collaborate with stakeholders to design scalable solutions
• Manage and optimize our internal data pipeline that supports marketing, customer success and data science
• Focus on ingesting, storing, processing, and analyzing large datasets
• Be a technical leader of PAR’s big data platform that supports AI and BI products
• Work with infra and operations teams to monitor and optimize existing infrastructure
• Develop and implement pipelines that extract, transform, and load data into information products that help the organization reach its strategic goals
• Help define data governance policies and support data-versioning processes
• Occasional business trips are required
• Leverage AI tools to streamline data pipeline development, generate data insights, and automate reporting to accelerate engineering productivity and decision-making

• Lead the design and development of gold-tier DBT models and BigQuery resources
• Act as the senior technical authority on the team
• Serve as the primary liaison between local and global engineering teams
• Design, build, and optimize scalable ELT pipelines using Airflow, DBT, and BigQuery
• Establish robust data quality frameworks and observability practices
• Mentor data and analytics engineers

Lead Data Engineer

• Design, document, build, and maintain robust and efficient data pipelines that collect, process, and store data from various sources, including user interactions, listing details, and external data feeds.
• Develop data models that enable the efficient analysis and manipulation of data for merchandising optimization.
• Ensure data quality, consistency, and accuracy.
• Build scalable data pipelines (SparkSQL, Scala & Python) leveraging Airflow scheduler/executor framework
• Collaborate with cross-functional teams, including Data Scientists, Product Managers, Operations, Finance & fellow Software Engineers, to define data requirements, and deliver data solutions that drive merchandising and sales improvements.
• Contribute to the broader Data Engineering community at Airbnb to influence tooling and standards to improve culture and productivity.
• Improve code and data quality by leveraging and contributing to internal tools to automatically detect and mitigate issues

• Develop and maintain features for our flagship application Civic Searchlight for investigative journalism and other use cases
• Discover new data sources and build pipelines to integrate them with existing infrastructure
• Implement rigorous monitoring and testing procedures so the database stays reliable as coverage grows
• Help build new tools on top of the database, including improved search, alerts, automated summaries and AI-assisted features
• Contribute to architectural decisions as we move from prototype-scale to production infrastructure

Investigative Data Engineer

• Lead, coach, and mentor a high-performing team, setting clear expectations and providing ongoing development to ensure alignment and achievement of key business objectives.
• Own and evolve enterprise data platforms across all business units, ensuring scalability, reliability, and alignment to business and technology strategies.
• Define and enforce standardized data models, KPI definitions, and business logic across all reporting and analytics platforms, enabling a consistent and trusted view of business performance.
• Own the enterprise reporting platform strategy, including tooling, platform governance, vendor relationships, and cost management, ensuring solutions are standardized, cost-effective, and aligned with business needs.
• Improve data delivery velocity by establishing scalable engineering practices, development standards, and automation across data teams.
• Establish and maintain pragmatic data quality, governance, and monitoring practices to ensure data is accurate, reliable, and transparent across the organization.
• Partner with Business Intelligence (Store Operations), Digital, Finance, Supply Chain, and Operations teams to deliver high-quality data products aligned to business needs.
• Operational Excellence and Continuous Improvement - Improve reporting delivery processes, data quality monitoring, issue resolution, and service levels for reporting availability and accuracy.

Director, Data Engineering

• Develop, maintain, and monitor scalable data pipelines and integration workflows
• Support data ingestion, transformation, and storage across cloud and on-prem systems
• Ensure the reliability, quality, and performance of data platforms
• Proactively monitor pipelines, jobs, and databases for performance and issues
• Collaborate with Senior Data Engineers, Architects, and cross-functional teams
• Analyze and process diverse datasets including equipment telemetry & machine health data, sales, service, and customer data

Data Engineer I

• Collaborate with clients to understand business objectives and technical requirements, translating them into effective data solutions that drive value.
• Support end-to-end delivery of data initiatives: design, build, and optimize scalable data pipelines, data lakes and architectures using Fabric, and other modern tools (Databricks, Snowflake, Azure Data Services, etc.).
• Design and implement lakehouse architectures, following medallion architecture principles to ensure scalability, data quality, governance, and performance.
• Build and maintain parameterized ingestion frameworks for batch and near real-time pipelines.
• Design, implement, and operate CI/CD pipelines to enable reliable, automated deployments across environments.
• Define, promote, and enforce Fabric best practices.
• Work closely with cross-functional teams —data engineers, analysts, and business stakeholders—to deliver cohesive and impactful solutions.
• Stay up to date with offerings and emerging technologies, recommending improvements and innovative solutions to meet business needs.

Data Engineer Consultant – Microsoft Fabric

• Design and implement data integration solutions in collaboration with the Solution Engineer and data architects
• Support ingestion, transformation, and orchestration using ADF, Synapse pipelines, Databricks Workflows, and event‑based patterns
• Implement batch, incremental, and event‑driven ingestion using agreed reference architectures
• Maintain and extend data integration frameworks
• Contribute to data modeling standards (Dimensional, Data Vault, Lakehouse)
• Implement analytics‑ready models and semantic layers for BI and ML
• Apply agreed data contracts and integration patterns
• Implement data cataloging, lineage, and data quality controls
• Apply RBAC, ABAC, encryption, and auditability controls
• Optimize Databricks performance, cost, and storage layout
• Monitor and troubleshoot pipelines and workloads
• Support platform modernization, POCs, and migrations
• Contribute to engineering standards and participate in design reviews
• Partner with offshore teams, architects, and business stakeholders

• Partner closely with stakeholders to translate business requirements into Fabric-based data models, pipelines, and analytical solutions
• Contribute to the end-to-end design and evolution of a scalable enterprise analytics architecture using Microsoft Fabric
• Collaborate with Business SMEs and technical teams (Infrastructure, SAP, Non-SAP systems) to design and deliver sustainable, governed analytics solutions
• Preparation and facilitation of design workshops with documented outcomes (requirements, design, data models)
• Contributes to work effort estimation for new and enhanced analytics solutions
• Ensure solutions are scalable, secure, cost-effective, and aligned with BI strategy
• Design and develop high-performance data engineering components capable of processing large volumes of structured and semi-structured data
• Build reusable, enterprise-grade data models (Lakehouse and semantic models) aligned with architectural standards and best practices
• Contribute to administration, governance, and operational health of the Microsoft Fabric platform
• Define and execute test strategies, including data validation, performance tuning, and data profiling
• Monitor Fabric pipelines, data refreshes, and workloads, proactively troubleshoot and resolve issues
• Own incident resolution and day-to-day sustainment of Fabric analytics solutions
• Utilize Microsoft Analytics tools for seamless integration and efficient user interface development to enhance overall user experience
• Optimize SQL, Spark, and DAX queries (added advantage) for performance and scalability
• Coach and mentor analytics, functional and business teams on Microsoft Fabric capabilities, patterns, and best practices
• Operates independently while ensuring alignment with enterprise BI and analytics strategy
• Capable of leading technical delivery, including design, build, test, and deployment phases
• Oversees project and support activities such as troubleshooting, enhancements, capacity planning, upgrades, and migrations
• Effectively manages multiple initiatives in parallel within a complex, fast-paced environment
• Communicates clearly and collaborates effectively with cross-functional and geographically distributed teams

IT Specialist, Enterprise Applications – Data Engineering Professional, Analytics

• Design, build, and maintain data pipelines that ingest and unify data from SaaS platforms, APIs, and internal systems
• Build and improve ELT/ETL workflows and support a reliable, well-structured data warehouse
• Develop and maintain consistent data models and definitions across core business entities
• Improve pipeline reliability through testing, monitoring, alerting, and automated issue resolution
• Work with technical and business stakeholders to solve data integration challenges and improve data trust
• Support strong data practices across data quality, documentation, governance, and warehouse operations
• Lead integration work across systems such as Salesforce and other key business platforms
• Contribute to data architecture decisions and help establish consistent engineering practices

• Design and build advanced reusable and automated frameworks for enterprise-scale data processing and analytics
• Architect comprehensive data solutions integrating multiple Azure services, AI capabilities, and cloud technologies aligned with strategic business goals
• Lead complex technical problem-solving initiatives, root cause analysis, and strategic decision-making for critical data systems
• Mentor junior and intermediate data engineers on advanced concepts, best practices, and emerging technologies
• Collaborate with senior stakeholders to influence business requirements and align technical solutions with enterprise objectives
• Develop AI-powered solutions and automation frameworks that drive measurable business outcomes
• Champion data engineering best practices, standards, and innovation across the organization through thought leadership

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