Data Engineering Manager

Arintra; Bengaluru; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

Arintra in Bengaluru is seeking a Senior Data Platform Lead who can architect and own large-scale data systems end-to-end. You will guide the platform from batch to streaming, set data contracts, and collaborate with product teams to deliver trusted analytics.

You bring 9+ years in software or data engineering, with 6+ hands-on in data engineering and 2+ years of directly managing data engineers. You will mentor, hire, and drive reliability, quality, and cost optimization across multi-source

Qualifications

• At least 9 years in software or data engineering.
• 6+ years hands-on data engineering experience.
• 2+ years directly managing data engineers.
• Proven production data platform ownership end-to-end.

Responsibilities

• Design and own production data platforms (batch and streaming).
• Own data quality, reliability: DQ checks, SLAs, incidents and RCAs.
• Mentor and grow a small senior team; hire and performance reviews.

Skills

Python

SQL

Data engineering

Leadership

Hiring

1:1s

Tools

BigQuery

Dataflow

Pub/Sub

Composer

Kafka

Debezium

Airflow

Job description

Requirements:

• 9+ years in software or data engineering, with 6+ hands-on in data engineering and 2+ years directly managing data engineers (hiring, performance, 1:1s).
• Came up as a strong engineer: wrote and owned production data code in Python and SQL, and can still sketch and review designs.
• Designed and owned a production data platform end-to-end (batch and streaming, orchestration, warehouse, or lakehouse) and built it rather than inherited it
• Owned data quality and reliability: DQ checks, pipeline SLAs, incidents, and RCAs in production.
• Built data for ML consumers: training and evaluation datasets, dataset versioning, feature pipelines, and human feedback or labeling loops.
• Strong statistical and analytical grounding, or building the data layer of an analytics product: metric design, sampling for data audits, anomaly and drift detection, cohort or experiment analysis, or a metrics or semantic layer, customer-facing dashboards, and reporting APIs that clients rely on.
• GCP depth (BigQuery, Dataflow, Pub/Sub, Composer); strong AWS or Azure equivalents are acceptable.
• Multi-source ingestion and CDC: Kafka or Pub/Sub, Debezium, and onboarding many external client data sources with different schemas.
• Data contracts, lineage, reconciliation across systems, and cost optimization with real numbers.
• Hired and grew a small senior team; explains data risk and trade-offs in business terms.