Genesys empowers organizations of all sizes to improve loyalty and business outcomes by creating the best experiences for their customers and employees. Through Genesys Cloud, the AI-powered Experience Orchestration platform, organizations can accelerate growth by delivering empathetic, personalized experiences at scale to drive customer loyalty, workforce engagement, efficiency and operational improvements.
We employ more than 6,000 people across the globe who embrace empathy and cultivate collaboration to succeed. And, while we offer great benefits and perks like larger tech companies, our employees have the independence to make a larger impact on the company and take ownership of their work. Join the team and create the future of customer experience together.
The Senior Data Engineer is a key member of the Data Engineering team within the Enterprise Data & Analytics organization. This role brings engineering excellence across design, delivery, and operations, building scalable data capabilities that transform how the business operates and makes decisions.
The team builds high-quality data products, analytical datasets, and consumption capabilities that serve enterprise reporting, analytics, AI/ML workloads, and downstream operational systems. This role is ideal for a strong data engineer who is also a self-starter and go-getter, with hands-on exposure to AI-enabled data solutions such as knowledge bases, vector databases, and intelligent data workflows.
Design, build, and maintain robust batch and real-time data pipelines using modern cloud data architectures.
Develop and optimize ETL/ELT pipelines to ingest, transform, and model data from a wide variety of enterprise sources.
Maintain and optimize data infrastructure to ensure reliability, scalability, and performance across large datasets.
Work closely with Data Architects to deliver solutions aligned with long-term enterprise data architecture and standards.
Ensure data accuracy, integrity, privacy, security, and compliance, including adherence to SOX and enterprise governance requirements.
Monitor data systems, identify bottlenecks, and implement performance tuning and operational improvements.
Follow and contribute to data engineering best practices, frameworks, and standards; actively help mature the data engineering function.
Mentor and guide junior engineers through code reviews, design discussions, and best-practice sharing.
Build and support AI-ready analytical datasets that power downstream ML, GenAI, and intelligent automation use cases.
Design and implement AI knowledge bases leveraging structured and semi-structured analytical data.
Contribute to agentic or AI-assisted data solutions, such as retrieval-augmented generation (RAG), data exploration agents, or intelligent analytics workflows.
Integrate vector databases and embeddings into data pipelines to enable semantic search and knowledge retrieval use cases.
Collaborate with Analytics, Data Science, and AI teams to productionize AI workflows with strong data foundations.
Stay current with evolving AI data patterns, tooling, and architectures, and proactively apply learnings to real business problems.
Bachelorâs degree in Computer Science, Information Systems, Engineering, or a related field.
5+ years of hands-on experience in data engineering or data platform development.
Strong experience with cloud data warehouses, especially Snowflake.
Experience building solutions on cloud platforms such as AWS, Azure, or GCP.
Experience working with CRM or enterprise systems such as Salesforce (or similar).
Strong SQL expertise and experience with data ingestion tools such as Fivetran, Talend, or equivalent.
Hands-on experience working with large-scale datasets and distributed data processing frameworks (Spark, Hive, Hadoop).
Strong proficiency in Python and/or Java, with solid object-oriented design principles.
Experience with workflow orchestration tools such as Apache Airflow.
Solid understanding of data modeling, database design, and performance optimization.
Hands-on experience building or supporting AI workflows, knowledge bases, or RAG-style solutions.
Exposure to vector databases (e.g., Pinecone, Weaviate, FAISS, OpenSearch, or similar).
Understanding of embeddings, semantic search, and AI-driven data consumption patterns.
Experience enabling data for ML/AI pipelines, even if not a full-time ML engineer.
Demonstrated curiosity and initiative in learning and applying modern AI technologies to data problems.
Proven ability to work independently and collaboratively in a global team environment.
A self-starter with strong ownership mindset and bias for action.
Strong communication skills with the ability to engage technical and non-technical stakeholders.
Comfortable operating in an Agile delivery model, collaborating across time zones.
Ability to learn quickly, adapt to change, and continuously improve engineering practices.
Reliable, scalable data pipelines powering analytics and AI use cases.
High-quality, well-governed data products trusted across the enterprise.
Tangible contributions to AI-enabled data solutions beyond traditional reporting.
Mentorship impact and visible elevation of team engineering standards.
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About Genesys:
GenesysŽ empowers more than 8,000 organizations worldwide to create the best customer and employee experiences. With agentic AI at its core, Genesys Cloud⢠is the AI-Powered Experience Orchestration platform that connects people, systems, data and AI across the enterprise. As a result, organizations can drive customer loyalty, growth and retention while increasing operational efficiency and teamwork across human and AI workforces. To learn more, visit www.genesys.com.
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