Skills

Must have

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
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.

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.

Data Engineering Manager

Compensation

Not disclosed

Location

Bangalore

Work setup

Not specified

Seniority

Not specified

Employment

Not specified

Experience

2+ years

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
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.

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.