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.

- Solid 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.

📌 Data Engineering Manager (Bengaluru)
🏢 Ontime Global
📍 Bengaluru