Sr Data Engineer
UKG · Bengaluru
Why UKG:
At UKG, the work you do matters. The code you ship, the decisions you make, and the care you show a customer all add up to real impact. Today, tens of millions of workers start and end their days with our workforce operating platform. Helping people get paid, grow in their careers, and shape the future of their industries. That’s what we do.
We never stop learning. We never stop challenging the norm. We push for better, and we celebrate the wins along the way. Here, you’ll get flexibility that’s real, benefits you can count on, and a team that succeeds together. Because at UKG, your work matters—and so do you.
About the Role
We are seeking a Senior Data Engineer to join our Data Warehouse, Business Intelligence, and Analytics team. This role is ideal for an experienced engineer who can build scalable cloud data pipelines, create trusted data products, and partner effectively with business and finance stakeholders.
The ideal candidate has hands-on experience with GCP and BigQuery, strong knowledge of modern data warehouse architecture, and a proven ability to deliver in Agile environments. Experience with Power BI, finance data, medallion architecture, and agentic AI tools such as Claude or Codex is highly desirable.
This is an end-to-end, AI-forward role. The Senior Data Engineer will work directly with stakeholders to define problems, design and build solutions, ensure data quality, support adoption, and drive continuous improvement. Success means owning trusted outcomes—not simply delivering code—while using automation and AI to improve the delivery lifecycle.
Key Responsibilities
- Partner with business and finance stakeholders to frame data problems and deliver end-to-end solutions.
- Design, develop, and maintain scalable data pipelines and ELT processes using GCP and BigQuery.
- Build and maintain layered data platforms using medallion architecture patterns, including Bronze, Silver, and Gold layers.
- Create curated data products, analytical datasets, and semantic models that support reporting, analytics, and decision-making.
- Integrate data from enterprise applications, finance systems, operational databases, APIs, and other source platforms.
- Conduct data profiling, source-to-target mapping, reconciliation, cataloging, and quality assessments.
- Translate stakeholder requirements into practical, scalable data solutions.
- Document designs, data flows, lineage, transformation logic, and operating procedures.
- Support, troubleshoot, and enhance data pipelines and interim legacy systems.
- Apply standards for security, performance, scalability, testing, monitoring, and governance.
- Contribute to sprint planning, backlog refinement, estimation, demos, and continuous improvement.
- Use automation, generative AI, and agentic tools to improve engineering productivity and delivery processes.
- Apply AI across the data engineering lifecycle, including discovery, mapping, SQL and pipeline generation, testing, quality rules, documentation, and lineage.
- Build AI-native data solutions such as natural-language data access, data agents, RAG/context layers, embeddings, and semantic search.
- Evaluate and operationalize AI tooling, including agent orchestration, tool/function calling, MCP servers, prompt and context engineering, evaluations, and guardrails.
- Curate metadata, definitions, and context that make data usable by people and AI agents.
- Own delivery from design through deployment, adoption, and ongoing enhancement, with accountability for business value and reliable operations.
Required Qualifications and Experience
- 5+ years of experience in data engineering, data warehousing, ETL, or ELT.
- Hands-on experience with GCP and Google BigQuery.
- Strong SQL skills and practical Python experience for data engineering use cases.
- Strong understanding of data warehouse architecture, dimensional modeling, data integration, and transformation techniques.
- Experience designing and supporting production-grade data pipelines with monitoring, error handling, testing, and performance optimization.
- Working knowledge of source control, CI/CD, automated testing, and deployment practices.
- Experience working in Agile teams.
- Strong verbal and written communication skills, with the ability to collaborate across technical teams, product owners, analysts, and business stakeholders.
- Strong problem-solving skills, initiative, adaptability, attention to detail, and sound technical judgment.
Preferred Qualifications
- Experience implementing medallion architecture or similar layered data-platform patterns.
- Experience developing reports, dashboards, or semantic models in Microsoft Power BI.
- Experience with finance, accounting, general ledger, accounts payable, accounts receivable, financial reporting, or reconciliation data.
- Knowledge of financial data models, accounting concepts, performance metrics, and reconciliation processes.
- Experience with additional GCP services such as Cloud Storage, Dataflow, Dataproc, Cloud Composer, Pub/Sub, or Cloud Functions.
- Experience with data governance, metadata management, data cataloging, and data lineage.
- Hands-on use of generative, agentic coding, or AI tools such as Claude or Codex for coding, automation, analysis, documentation, or workflow orchestration.
- Experience with prompt and context engineering, LLM APIs, RAG, embeddings or vector search, agent orchestration, or AI-assisted automation.
- Experience contributing to analytics products from discovery and design through delivery, adoption, and enhancement.
- Experience owning solutions from business problem definition to adopted, quality-assured production data products.
What Success Looks Like
- Within 90 days, key production data pipelines have documented monitoring, error handling, support procedures, and clear ownership.
- High-priority BigQuery datasets have clear definitions, lineage, quality checks, and stakeholder sign-off for reporting, analytics, and finance use cases.
- Business stakeholders, analysts, product owners, and engineering partners consistently rely on the role for solution design, delivery updates, issue resolution, and production support.
- Automation and responsible AI practices measurably reduce manual effort, improve documentation quality, and make recurring delivery tasks more repeatable.
- Within 6–12 months, at least one agentic or AI-enabled data solution is deployed to production and adopted for a defined business or engineering use case.
Company Overview:
UKG is the Workforce Operating Platform that puts workforce understanding to work. With the world's largest collection of workforce insights, and people-first AI, our ability to reveal unseen ways to build trust, amplify productivity, and empower talent, is unmatched. It's this expertise that equips our customers with the intelligence to solve any challenge in any industry — because great organizations know their workforce is their competitive edge. Learn more at ukg.com.
UKG is proud to be an equal opportunity employer and is committed to promoting diversity and inclusion in the workplace, including the recruitment process.
Disability Accommodation in the Application and Interview Process
For individuals with disabilities that need additional assistance at any point in the application and interview process, please email UKGCareers@ukg.com