ABOUT THE ROLE

Own data that has to be right, every time.

You will own the data foundation for a high-volume client engagement where every number has to reconcile.

You design the pipelines, keep them correct through scale and schema change, and balance cost, reliability and data integrity. The role demands architectural judgement and hands-on engineering in equal measure.

RESPONSIBILITIES

What you will own

01 Data architecture and pipelines

- Own ingestion, transformation and serving pipelines for high-volume event data on AWS, Databricks and

PySpark.
- Make build-versus-buy calls that balance cost, latency and reliability.
- Keep the lakehouse coherent as sources, volumes and schemas evolve.
- Manage pipeline infrastructure as code, with CI/CD for data jobs and orchestration through Airflow or

Databricks Workflows.

02 Scale, cost and performance

- Control cloud cost through partitioning, file formats and compute sizing.
- Track cost per pipeline and cost per query as first-class metrics.
- Diagnose and fix bottlenecks in Spark jobs and downstream serving layers.

03 Data quality and reconciliation
- Build reconciliation and quality checks that catch discrepancies before reporting or billing.
- Monitor and alert on pipeline failures, schema drift and silent data loss.
- Hold the line on accuracy where downstream business logic depends on it.

04 Governance and standards
- Define schema contracts, naming conventions and documentation standards across pipelines.
- Set access and data-handling practices that hold up under client and regulatory scrutiny.




- Review changes to shared datasets so downstream consumers get warning before any break.

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05 Technical leadership and client partnership

- Direct engineers on the engagement without formal reporting lines.
- Represent data engineering with the client: explain trade-offs, defend decisions, flag risk early.
- Mentor engineers through design reviews, debugging sessions and code review.

PROFILE

What makes you a robust fit

✓ 8-12 years in data engineering, with ownership of large-scale production pipelines.

✓ Deep hands-on AWS, Databricks and PySpark experience. You debug the job when it breaks.

✓ Systems that absorb schema evolution and upstream change while downstream consumers keep running.

✓ Experience in high-volume, high-stakes environments where accuracy and timeliness carry business consequences.

✓ Cost-aware instincts: you have cut a cloud bill or rescued a runaway pipeline.

✓ Hands-on with infrastructure as code (Terraform or equivalent), workflow orchestration and CI/CD for data pipelines.

✓ Comfort with consulting accountability: client scrutiny, shifting priorities and a high bar for communication.

THE WORK · WHAT YOU WILL LEAD The data engineering foundation for a high-volume enterprise engagement, spanning architecture,

schema governance, cost optimisation and data quality at scale. The scope may extend to other clients with similar needs. You own outcomes: pipelines that run, numbers that reconcile and a system the client trusts.

📌 Lead Data Engineer [8+yrs] (Bengaluru)
🏢 Nasugroup.com
📍 Bengaluru