Empower your business with custom data engineering solutions designed to optimize operations and unlock the full potential of your enterprise data. From scalable pipelines to real-time processing and advanced DataOps, our expertise ensures your data fuels smarter decisions and competitive growth. Our data engineering expertise has earned the trust of renowned enterprises across industries. Why do businesses choose us?
Leaders trusting our data engineering solutions:
10+
years empowering enterprises with data-driven strategies
50+
successful large-scale projects completed
40%
reduction in development costs on average for our clients
Proud members and partners of
Xenoss collaborates with leading industry organizations and standards bodies to advance AI and Data Engineering development
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Master key concepts and terminology in AI and Data Engineering
Next steps for your data engineering transformation journey
Strategize with Xenoss
Partner with our specialists to define strategic priorities, assess the current data landscape and identify gaps, and prioritize use cases. We help you select core technologies, ensure data and IP security, and align your data strategy with business goals, processes, and key stakeholders — all to create measurable business impacts
Design the blueprint with Xenoss
Work closely with our experts to conduct a comprehensive discovery and assessment phase, defining your use case objectives and setting clear goals. We help you select the right frameworks and technology accelerators for your needs, and collaboratively design architecture patterns, addressing constraints and finalizing recommendations—all to ensure an efficient and scalable data solution
Build with Xenoss
Collaborate with Xenoss to refine or validate your existing architecture, develop a working prototype with expert support, and create a clear roadmap for production. Together, we’ll ensure your solution is built for real-world success
Data engineering suite: Capabilities for complex, scalable systems
With extensive experience in data engineering, Xenoss builds specialized teams to deliver efficient, customized data solutions that address your unique, industry-specific, and enterprise-scale challenges
Data fabric
We architect data fabrics that unify access to distributed data sources, connecting silos across cloud, on-premise, and edge, with built-in lineage, governance, and schema control.
Data platforms
We build custom platforms to support ingest, storage, and analytics at scale, integrating tools like Kafka, Spark, Delta Lake, or BigQuery to match your data ecosystem.
Data pipelines
We engineer batch, micro-batch, and real-time pipelines for ingestion, transformation, and routing, with monitoring, retries, and data quality checks baked in.
Data infrastructure
Xenoss delivers complete data infrastructure solutions that enable efficient data storage and processing, providing scalability, reliability, and security for all of your data operations.
Data migration
We execute secure, zero-downtime data migrations between platforms, ensuring schema integrity, consistency, and pipeline continuity during upgrades or stack transitions.
Data mining and preparation
Our data mining and preparation services help you cleanse and transform raw datasets into clean, structured, and ready-to-use data to fuel downstream processes and analytics.
Infrastructure cost optimization
We identify redundant pipelines, inefficient queries, and underutilized resources and then re-architect for cost efficiency without sacrificing performance.
Data stack integration
We provide data stack integration services to connect your tools, from ingestion to BI, into a seamless, reliable stack using message buses, orchestration frameworks, and custom APIs.
Real-time data solutions
We build stream-first architectures that support low-latency processing, push-based analytics, and live monitoring across high-frequency systems.
Data analytics solutions
Xenoss builds data analytics solutions that help you generate actionable insights, providing visibility into key metrics to inform better business and engineering decisions.
Data visualization & reporting solutions
We develop visualization and reporting tools that turn complex data into understandable visuals, making it easier for you and your stakeholders to interpret and act on key insights.
Enterprise application modernization services
We decouple monoliths, migrate legacy apps to modern data frameworks, and wrap legacy logic in scalable, API-first infrastructure, future-proofing your architecture.
Data strategy
Our data strategy services help engineering leaders design a data roadmap, ensuring your organization gets the most value out of its data assets and is set up for long-term success.
Data engineering consulting
We provide expert consulting on data architecture, helping you design, build, and optimize your data infrastructure for efficiency, reliability, and scalability.
Business intelligence
Xenoss delivers BI solutions that empower data teams to extract actionable insights, enabling data-driven strategies and enhancing decision-making across the organization.
Cloud solutions
Build products from scratch to ensure their scalability, load tolerance, and facilitated maintenance.
From ingestion to orchestration and delivery, we help enterprises modernize, optimize, and operationalize their data stack
Common data engineering challenges we solve
Inconsistent data integration
Connect and unify data from multiple systems, formats, and ingestion protocols with schema enforcement and transformation logic.
Low data quality
Automate detection, cleansing, deduplication, and enrichment of fragmented or messy datasets to improve model readiness and reporting accuracy.
Limited scalability of data pipelines
Redesign batch and stream pipelines for performance at scale with partitioning, queuing, and orchestration built for high-throughput workloads.
Inability to process real-time data
Build low-latency data systems using Kafka, Flink, or Spark Structured Streaming, enabling sub-second decisioning and alerting.
Data security vulnerabilities
Implement end-to-end encryption, access controls, backups, and monitoring to secure pipelines and meet compliance requirements like GDPR or SOC 2.
High costs and suboptimal storage solutions
Optimize resource usage with auto-scaling architectures, columnar storage formats, and query-efficient design patterns.
Insufficient pipeline monitoring and debugging
Embed observability into your pipelines with metrics, logs, alerting, and automated error recovery to reduce data downtime.
Data siloing across departments
Facilitate cross-department communication with all-in-one platforms designed by Xenoss engineers.
Data duplication and redundancy
Eliminate duplicate records and noisy joins by enforcing schema standards, matching keys, and integrating deduplication logic directly into pipelines.
Repetitive tasks
Automate routine data engineering workflows — from validation and enrichment to deployment and alerting, using triggers, DAGs, and CI/CD for pipelines.
Challenges with data archiving and retention
Implement intelligent archiving strategies with automated tiering, TTL policies, and storage rules that align with compliance and performance goals.
Lack of transparency in data lineage and governance
Enable full visibility into data flow, ownership, and transformations through metadata tracking, lineage visualization, and governance frameworks (e.g., DataHub, Marquez).
How to start
Transform your enterprise with AI and data engineering—faster efficiency gains and cost savings in just weeks
Challenge briefing
2-3 hours
Tech assessment
2-3 days
Discovery phase
1 week
Proof of concept
8-12 weeks
MVP in production
2-3 months
Data tech stack behind Xenoss’ projects
Empowering enterprises through hyperscalers and technology data toolset
Why Xenoss is trusted to build enterprise-grade data systems
World’s leader in streaming pipelines
We build ultra-low-latency streaming architectures that process petabytes of data in real time, supporting fraud detection, personalization, and operational decisions at scale.
Proven performance at high load
Our systems power millions of daily operations across mission-critical workloads, engineered for throughput, fault tolerance, and global availability.
Proven enterprise track record
We’ve delivered complex, scalable platforms for large-scale enterprises, integrating across cloud, data lakes, legacy tools, and modern ML ops.
Speed of delivery
Utilize Xenoss’s pre-built development accelerators to get your data solutions delivered extremely fast.
Multi-cloud engineering
We design hybrid and multi-cloud architectures optimized for resilience, cost control, and latency, integrating across AWS, Azure, GCP, and on-premise.
Tech agnostic
We adapt to your tech stack and ecosystem, Spark or Flink, Kafka or Pulsar, dbt or Airflow, without pushing any vendor lock-in.
End-to-end solution architecture
Work with experts who bridge the gap between business challenges and technical solutions, ensuring seamless integration from strategy through implementation and beyond.
Business-aligned engineering
Partner with Xenoss team that combines technical expertise with business acumen, delivering solutions that align perfectly with your enterprise objectives and drive measurable ROI.
Featured projects
AdTech
Implementing a proprietary SDK with a lightweight tracker for a new generation mediation platform
Modernize your data stack with real AI & Data engineering
Talk to our engineers about designing custom pipelines, streaming infrastructure, or AI-ready data architectures. Whether you’re modernizing, scaling, or starting from scratch, we’ll help you move fast and build right.
Xenoss team helped us build a well-balanced tech organization and deliver the MVP within a very short timeline. I particularly appreciate their ability to hire extreme fast and to generate great product ideas and improvements.
Oli Marlow Thomas,
CEO and founder, AdLib
Get a free consultation
What’s your challenge? We are here to help.
Leverage more data engineering & AI development services
A data engineering consultant helps organizations design, build, and optimize data infrastructure for effective data-driven decision-making. They provide data engineering consulting services like developing data pipelines, data lakes, and analytics. Consultants from data engineering companies also assist with data strategy and business use case solutioning.
Whether exploring data engineering with AWS, AI strategy, or data engineering in finance, data engineering consulting firms provide tailored solutions, including data engineering as a service. By partnering with data engineering companies or service providers, businesses can leverage expertise to build reliable and future-proof data systems.
What is the difference between a data engineer and a data consultant?
A data engineer focuses on building, developing, and maintaining data infrastructure, such as data pipelines, data lakes, and data warehouses. They ensure that data is collected, stored, and made accessible efficiently for analysis. Data engineering consulting services often involve designing scalable data solutions, managing data quality, and implementing technologies like AWS for data processing.
On the other hand, a data consultant works more strategically, helping companies understand their data needs and advising on how to best leverage their data for decision-making and achieving business objectives. They often work with stakeholders to define data strategies and align technical capabilities with business goals. While data engineers are hands-on with coding and system architecture, data consultants take a more advisory role, which can involve data strategy development, assessing business use cases, and recommending data solutions.
In short, data engineers are builders, focusing on technical implementation, while data consultants are advisors, focusing on strategic alignment and planning.
What does a data engineer do in a company?
A data engineer is responsible for designing, building, and managing the data infrastructure that supports a company’s analytics and business intelligence needs. This includes creating data pipelines that extract, transform, and load (ETL) data from various sources into data warehouses or data lakes, ensuring that the data is reliable, scalable, and available for analysis.
They also work to optimize data flow, improve data quality, and make sure that data systems are efficient and secure. Data engineers often collaborate closely with data scientists, analysts, and other business teams to understand data needs and provide the necessary infrastructure for insights and decision-making. They use technologies such as AWS, Spark, or SQL to create solutions that transform raw data into structured formats that the company can leverage for analytics and reporting.
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