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