Senior Software Engineer – Data Pipelines (Java)
Fivetran · Bengaluru
| Company | Fivetran |
|---|---|
| Role | Senior Software Engineer – Data Pipelines (Java) |
| Location | Bengaluru |
| Work mode | Hybrid |
| Experience | 3–6 years |
| Job type | Full-time |
About this job
Fivetran (now combined with dbt Labs) is hiring a Senior Software Engineer in Bengaluru for its data pipeline service. The team builds the pipelines and warehouse data writers that move terabytes of customer data from many sources into cloud data warehouses.
The work ranges from bug fixes and patches to building new warehouse writers and tuning sync services on a global Kubernetes cluster. As a senior member you will own features, lead design reviews, write code and mentor junior engineers. The role is hybrid.
Eligibility
- 5+ years of professional Java development in distributed production systems
- Experience with reliable, data-heavy systems that run under strict SLAs
- Good understanding of complex SQL merge queries and query plans
- Strong debugging skills and the ability to write clear root cause analyses
- Comfort using AI coding assistants as part of daily development
Skills required
What you will do
- Build and improve the data writer platform that loads data into warehouses
- Process and split data into CSV, Avro and Parquet files, then merge and load it
- Work with cloud APIs, SQL databases and warehouses such as BigQuery and Databricks
- Own features end to end, pitch designs and take part in design reviews
- Fix bugs and make steady improvements to the core pipeline
How to apply
- Open the official Fivetran job page with the Apply button and submit your application there.
- Tailor your resume to Java services, data volumes, SQL tuning and any warehouse loading or ETL work you have done.
Before the interview, prepare:
- SQL MERGE and upserts: how warehouses apply inserts, updates and deletes in bulk
- File formats: when to use CSV, Avro or Parquet and how compression and schemas differ
- Java concurrency: thread pools, CompletableFuture and memory issues in long-running jobs
- Debugging under SLA: walk through a production issue and how you found its root cause
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