Engineering Manager - Data Engineering

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

Job summary

Arintra in Bengaluru seeks a Senior Data Platform Lead to own end-to-end data platform development, from design to production support. You will mentor a team of data engineers while hands-on coding in Python and SQL, shaping pipelines for batch and streaming workloads.

Responsibilities include architecture reviews, data quality, performance optimization, and ML data pipelines, with a focus on scalable, cost-efficient cloud solutions and strong collaboration with business stakeholders.

Qualifications

• 9+ years in software or data engineering, incl. 6+ years hands-on data engineering and 2+ years of people management.
• Proven production Python and SQL experience; able to review code and architectures.
• Experience owning end-to-end data platforms from scratch.
• Strong knowledge of batch/streaming pipelines, orchestration, and data warehouse/lakehouse.
• Proven data quality, SLA, incident RCA ownership.
• Experience mentoring data engineers.

Responsibilities

• Lead and mentor a team of senior data engineers, including hiring, performance reviews, regular 1:1s, and technical development.
• Design, build, and scale production-grade data platforms from scratch, covering batch and streaming pipelines, orchestration, and data warehouse/lakehouse infrastructure.
• Lead system design discussions, review data schemas and architecture, conduct code reviews, and stay hands-on with Python and SQL.
• Own data quality checks, pipeline SLAs, production incidents, and root‑cause analysis.
• Oversee multi-source data ingestion, CDC, data lineage, data contracts, and cross-system reconciliation.
• Build and support data pipelines for ML training/evaluation, dataset versioning, feature pipelines, and human-in-the-loop workflows.
• Optimize cloud infrastructure and data processing costs while communicating trade-offs to stakeholders.

Skills

People management

Python

SQL

System design

Data quality

Education

Bachelor's degree in Computer Science or related field

Tools

Airflow

Spark

Snowflake

Kubernetes

Job description

Responsibilities

• Engineering Leadership and People Management: Manage and mentor a team of senior data engineers, including hiring, performance reviews, regular 1:1s, and technical development.
• End-to-End Data Platform Ownership: Design, build, and scale production-grade data platforms from scratch, covering batch and streaming pipelines, orchestration, and data warehouse/lakehouse infrastructure.
• Technical Leadership and Architecture: Lead system design discussions, review data schemas and architecture, conduct code reviews, and remain hands‑on with Python and SQL.
• Data Quality and Reliability: Own data quality checks, pipeline SLAs, production incidents, and root‑cause analysis to ensure reliable data operations.
• Data Ingestion and Integration: Oversee multi‑source data ingestion, Change Data Capture (CDC), data lineage, data contracts, and cross‑system data reconciliation.
• ML and Analytics Enablement: Build and support data pipelines for ML training and evaluation, dataset versioning, feature pipelines, and human‑in‑the‑loop feedback workflows.
• Performance and Cost Optimization: Optimize cloud infrastructure and data processing costs while managing technical risks and communicating trade‑offs to business stakeholders.

Requirements

• Experience: 9+ years of overall software or data engineering experience, including 6+ years of hands‑on data engineering experience and at least 2 years of direct people management of data engineers.
• Strong Technical Foundation: Proven hands‑on experience writing and owning production code in Python and SQL, with the ability to review code, evaluate schemas, and critique architectural designs.
• Data Platform Ownership: Experience designing, building, and owning an end‑to‑end production data platform from the ground up, rather than simply maintaining an existing platform.
• Technical Architecture: Strong understanding of data platform architecture, including batch and streaming pipelines, orchestration, and data warehouse/lakehouse infrastructure.
• Production Reliability: Experience owning data quality checks, pipeline SLAs, production incident management, and root‑cause analysis (RCA).
• People Management: Proven experience directly managing, mentoring, and developing data engineers while remaining technically hands‑on.