Senior Data Engineer

# Senior Data Engineer

**Company:** Velozent
**Employment Type:** Full-time
**Workplace:** Hybrid (3 days a week in office)
**Location:** Bengaluru, India
**Experience:** 5+ years
**Salary Range:** INR 30,00,000–45,00,000 per year

Velozent is hiring a Senior Data Engineer to design, build and run the data platform behind our products. You'll own batch and streaming pipelines end to end: ingesting events from Kafka, transforming them with Spark on AWS, and serving clean, well-modelled data to analytics, product and ML teams. This is a hands-on senior role with real ownership. You'll set standards for data quality, cost and reliability, and mentor two to three engineers.

### Key Responsibilities

- Design, build and maintain scalable batch and real-time pipelines using Python, PySpark and Apache Spark (Structured Streaming).
- Own our AWS lakehouse (S3, Glue, EMR/Databricks, Delta Lake, Redshift), including partitioning, file layout, compaction and cost.
- Orchestrate workflows with Apache Airflow: dependency design, SLAs, retries, backfills and alerting.
- Build streaming ingestion from Apache Kafka with exactly-once or idempotent processing guarantees.
- Model data for analytics (dimensional / data vault) and publish trusted, documented datasets.
- Put data-quality checks, lineage and observability in place so bad data is caught before it reaches a dashboard.
- Tune Spark jobs (joins, skew, shuffle, caching, cluster sizing) for performance and cloud-cost efficiency.
- Manage infrastructure as code with Terraform, and deploy through CI/CD.
- Review code, mentor engineers, and work with product, analytics and ML teams on the data roadmap.

### Requirements

- 5+ years of data engineering experience, with at least 3 years on Spark / PySpark in production.
- Strong Python and advanced SQL (window functions, query plans, performance tuning).
- Deep hands-on AWS experience: S3, Glue, EMR, Redshift, IAM, and cost control.
- Production experience with Apache Airflow and Apache Kafka.
- Experience with Databricks and Delta Lake (or a comparable lakehouse such as Iceberg or Hudi).
- A solid grasp of distributed systems, data modelling and pipeline reliability.
- Clear written and spoken communication; comfortable owning decisions and trade-offs.

### Nice to Have

- AWS Certified Data Engineer or Databricks Data Engineer Professional certification.
- Terraform / infrastructure-as-code in production.
- dbt, Snowflake or Scala.
- Experience supporting ML feature pipelines or real-time analytics.

### What We Offer

- Competitive CTC with annual performance bonus and ESOPs.
- Hybrid work from our Bengaluru office, with flexible hours.
- Health insurance for you and your family, and a learning and certification budget.
- A small, senior team where your work ships to production every week.

### Hiring Process

1. Recruiter conversation (30 min)
2. Online technical assessment (45 min)
3. Technical panel: Spark, AWS and system design (60 min)
4. Hiring manager round and offer