Data Ops Lead

Full Time, onsite
Helius Technologies
Singapore, Singapore

Salary undisclosed

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Data Ops Lead

Job Requirement:

8 years of experience in data engineering, with expertise in AWS services, Databricks and ETL (Informatica).
The role is to manage IT Operations of the datawarehouse /data lake environment and the analytics components to ensure the effective planning, governance and execution to meet the operational service levels agreements and audit compliance requirements
We are looking for Candidate with experience in analytics tools and databases e.g. ETL, Databricks, AWS, Oracle, Tableau, DataRobot, Sagemaker, Informatica IDMC (good to have)

Requirement:

8 years of experience in developing, implementing and maintaining data projects or analytics systems with at least 8 years of project operation management experience
Highly-motivated self-starter who will undertake all activities to the highest professional standards
Cloud certified (Good to have)
Good in-depth understanding of data warehouse concepts, data profiling, data verification and advanced analytics techniques
Experience in managing a team in operations support and project implementation work
Certification in IT operation management is preferred

Preferred Skills (Good to have Skills)

Informatica IDMC
Experience with big data technologies like Apache Spark and Hadoop on Databricks.
Knowledge of data governance and data cataloguing tools, especially Informatica IDMC.
Familiarity with data visualization tools like Tableau or Power BI.
Knowledge of containerization and orchestration tools like Docker and Kubernetes.
Understanding of DevOps principles for managing and deploying data pipelines.
Experience with version control systems (e.g., Git) and CI/CD pipelines.

Roles And Responsibilities:

Design and architect data storage solutions, including databases, data lakes, and warehouses, using AWS services such as Amazon S3, Amazon RDS, Amazon Redshift, and Amazon DynamoDB, along with Databricks' Delta Lake. Integrate Informatica IDMC for metadata management and data cataloging.
Create, manage, and optimize data pipelines for ingesting, processing, and transforming data using AWS services like AWS Glue, AWS Data Pipeline, and AWS Lambda, Databricks for advanced data processing, and Informatica IDMC for data integration and quality.
Integrate data from various sources, both internal and external, into AWS and Databricks environments, ensuring data consistency and quality, while leveraging Informatica IDMC for data integration, transformation, and governance.
Develop ETL (Extract, Transform, Load) processes to cleanse, transform, and enrich data, making it suitable for analytical purposes using Databricks' Spark capabilities and Informatica IDMC for data transformation and quality.
Monitor and optimize data processing and query performance in both AWS and Databricks environments, making necessary adjustments to meet performance and scalability requirements. Utilize Informatica IDMC for optimizing data workflows.
Implement security best practices and data encryption methods to protect sensitive data in both AWS and Databricks, while ensuring compliance with data privacy regulations. Employ Informatica IDMC for data governance and compliance.
Implement automation for routine tasks, such as data ingestion, transformation, and monitoring, using AWS services like AWS Step Functions, AWS Lambda, Databricks Jobs, and Informatica IDMC for workflow automation.
Maintain clear and comprehensive documentation of data infrastructure, pipelines, and configurations in both AWS and Databricks environments, with metadata management facilitated by Informatica IDMC.
Collaborate with cross-functional teams, including data scientists, analysts, and software engineers, to understand data requirements and deliver appropriate solutions across AWS, Databricks, and Informatica IDMC.
Identify and resolve data-related issues and provide support to ensure data availability and integrity in both AWS, Databricks, and Informatica IDMC environments.
Optimize AWS, Databricks, and Informatica resource usage to control costs while meeting performance and scalability requirements.
Stay up-to-date with AWS, Databricks, Informatica IDMC services, and data engineering best practices to recommend and implement new technologies and techniques.