Tech Lead – Data Lake Platform

Tech Lead – Data Lake Platform

Recruitment & Payroll Company

Neogencode Technologies

Job Location

Sector 43, Gurugram, Haryana

Work Mode

On-site (Work from Office)

Working Days

5 days a week

Experience Required

5+ years

Employment Type

Full-time

About the Opportunity

We are hiring a Tech Lead – Data Lake Platform. This opportunity involves working on an enterprise-grade AWS data lake platform supporting data engineering, analytics, reporting, and business applications.

The candidate will be employed on the payroll of Neogencode Technologies and will work at the client location in Sector 43, Gurugram.

Key Responsibilities

Own and design end-to-end data lake architecture, including data ingestion, staging, curated layers, and data consumption.

Lead data engineering pipelines using AWS services, Apache Airflow, Spark/PySpark, and dbt.

Manage AWS data services such as S3, EMR, Glue, Athena, Redshift, Lambda, RDS, IAM, and DMS.

Design and support API/data-serving solutions using PostgreSQL, Hasura GraphQL, or similar technologies.

Improve pipeline performance, platform reliability, monitoring, and service-level agreements (SLAs).

Ensure data security, access control, personally identifiable information (PII) protection, and governance.

Mentor data engineers, conduct code reviews, and handle production incidents and root-cause analysis.

Collaborate with business and application teams to deliver data and reporting solutions.

Required Skills

5+ years of experience in data engineering or platform engineering, including technical leadership.

Strong hands-on experience with AWS data services and enterprise data lake architecture.

Production experience with Apache Airflow, Spark/PySpark, and SQL.

Strong understanding of data modeling and layered data architecture.

Experience with PostgreSQL and Hasura GraphQL or a similar API/data-serving layer.

Proven experience in technical leadership, production support, and troubleshooting.

Good to Have

Experience with dbt and lakehouse table formats.

Knowledge of Kafka or streaming data ingestion.

Experience with Redshift optimization and workload design.