Data Engineering Manager

Ontime Global; Bengaluru; On-site; INR 4,000,000 - 6,000,000; Full time

Job summary

Ontime Global in Bengaluru seeks a senior data engineering leader to design and implement large-scale data projects on cloud platforms (AWS/Azure/GCP). You will drive pre-sales engagement, develop PoCs, and architect secure data lakes and pipelines with Spark/Databricks.

You will mentor a team of data engineers, collaborate with data scientists and BI teams, and ensure data quality, governance, and cost efficiency across the organization.

Qualifications

• 5+ years in data engineering with data lake implementations.
• Strong cloud proficiency across AWS, Azure, or GCP.
• Experience building ETL/ELT pipelines with Spark/Databricks.
• Leadership experience guiding teams and handling pre-sales to delivery.
• Knowledge of data governance and security in cloud environments.

Responsibilities

• Lead design and implementation of large-scale data projects on cloud.
• Drive pre-sales and develop technical proposals and PoCs.
• Architect scalable data storage using S3/Data Lake Storage.
• Oversee ETL/ELT pipelines with Spark, Databricks, Glue/ADF.
• Ensure data quality and governance using cloud services.
• Collaborate with data scientists and BI teams.
• Mentor and manage data engineering team.
• Align deliverables with business goals.
• Monitor data processing efficiency and costs.

Skills

Data engineering

Cloud platforms

Databricks

Apache Spark

SQL

Python/Scala/Java

Leadership

Pre-sales

Data governance

Data warehousing

Communication

Tools

AWS

Azure

GCP

Kubernetes

Docker

ETL tools

Job description

Role & responsibilities

• Lead the design and implementation of large-scale data engineering projects, including data lakes and data pipelines on cloud platforms like AWS, Azure, or GCP.
• Drive the pre-sales process by engaging with clients, understanding requirements, and developing technical proposals and proof of concepts (PoCs).
• Architect scalable and secure data storage solutions using technologies like Amazon S3, Azure Data Lake Storage, and Google Cloud Storage.
• Oversee the development of ETL/ELT pipelines using tools such as AWS Glue, Apache Spark, Databricks, or Azure Data Factory.
• Ensure efficient data transformation and quality assurance processes by leveraging tools like AWS Lambda, Google Cloud Functions, or Azure Functions for serverless computing.
• Implement data governance frameworks to ensure data quality and compliance using services like AWS Lake Formation, Azure Purview, or Google Data Catalog.
• Collaborate with data scientists, BI developers, and analytics teams to ensure the smooth flow of data and insights across the organization.
• Manage a team of data engineers, providing technical guidance and mentorship throughout the project lifecycle.
• Engage with stakeholders and clients to align project deliverables with business goals.
• Monitor and optimize data processing and storage to ensure efficiency and cost-effectiveness.

Required Skills

• 5+ years of hands‑on experience in data engineering, including leading data lake implementations and cloud-based solutions.
• Proficiency in cloud platforms (AWS, Azure, or GCP) and services like Amazon S3, Azure Data Lake, Google Cloud Storage.
• Extensive experience with data transformation tools such as Apache Spark, Databricks, AWS Glue, and Azure Data Factory.
• Expertise in serverless architectures (e.g., AWS Lambda, Google Cloud Functions, Azure Functions).
• Strong understanding of data governance, data quality, and security best practices in the cloud.
• Familiarity with containerization and orchestration technologies such as Kubernetes and Docker.
• Proficiency in SQL, Python, Scala, or Java for data manipulation and processing.
• Experience working with data warehousing and analytics solutions like Amazon Redshift, Google BigQuery, or Azure Synapse.
• Strong leadership and project management skills, with a track record of managing teams and delivering complex projects from pre-sales to delivery.
• Excellent communication skills to effectively interact with stakeholders, clients, and technical teams.

Preferred Qualifications

• Experience with data governance tools like AWS Lake Formation, Azure Purview, or Google Data Catalog.
• Certifications in cloud platforms such as AWS Certified Solutions Architect, Azure Data Engineer, or Google Professional Data Engineer.
• Familiarity with CI/CD pipelines and DevOps practices in the context of data engineering.