DataOps/Cloud Data Engineer

Description To respond to the priorities of the Government of Ontario through the passage of the Bill 5, Protect Ontario by Unleashing Our Economy Act 2025, the resource may be partially assigned to Bill 5 related projects or initiatives on an as needed basis.

This position is listed as “Onsite” unless otherwise instructed by the Client; the resource is expected to work 7.25 hours per calendar day between the standard working hours of 8:00-5:00 PM (excluding lunch breaks) every Monday to Friday inclusive at the identified OPS office location.

Responsibilities

Designing and developing data pipelines from source to end user

Optimizing data pipelines

General Skills

Experience with Cloud data platforms, data management and data exchange tools and technologies

Experience with Commercial and opensource data and database development and management and specializing in data storage setting and managing cloud Data As a Service (DaaS). application Database as a Service (DBaaS), Data Warehouses as a Service (DWaaS), and other storage platforms (both in the cloud and on-premise).

Experience with Data pipeline and workflow development, orchestration, deployment, and automation and specializing in programming and pipelines to create and managing the dataflow and movement of data.

Experience with Cloud data engineer must be familiar and experienced with different programming languages and be able to integrate with many different platforms to create data pipelines, automate tasks, and write scripts.

Experience in DataOPS principle, best practice, and implementation and Agile project development and deployment

Experience in Continuous Integration/Continuous Development/Deployment (CI/CD) and Data provisioning Automation

Experience with digital product, data analysis, data exchange, data provisioning, and data security

Extensive expert experience in designing/developing and implementing data conversation and migration of VLD (Very large Data) of Online analytical processing (OLAP) and online transaction processing (OLTP) environments to Cloud Software-as-a-service (SaaS), Platform-as-a-service (PaaS) and Infrastructure-as-a-service (IaaS) environments.

Experience in design, development and implementation of fact/dimension model, data mapping, data warehouse, data lake and data lakehouse for enterprise

Experience managing Cloud Data services for project delivery, including storage, repositories, Data Lake, Data Lakehouse, key vault, virtual machine, disk, etc.

Experience with structured, semi-structured, unstructured data collection, ingestion, provisioning and exchange technological development of enterprise data warehouse and data lake and data lakehouse solutions and operational support.

Experience with DataOPS performance monitoring and tuning

Excellent analytical, problem-solving and decision-making skills; verbal and written communication skills; presentation skills; interpersonal and negotiation skills

A team player with a track record for meeting deadlines

Experience Requirements Following are the evaluation criteria along with the evaluation % weightage.

Evaluation Criteria / Evaluation % Weightage

Mandatory Skills and Experience – 70%

Desirable Skills and Experience – 25%

Education – 3%

Public Sector Experience – 2%

TOTAL – 100%

Mandatory Skills and Experience (8+ years experience):

Design, build, automate and optimize complex data ETL/ELT processes using Databricks, Delta Live Tables (DLT) and equivalent.

Development of unified data platforms using Databricks and Unity catalog or its equivalent.

Relational databases (Oracle, MySQL, SQL Server), data modelling (relational & dimensional), advanced SQL, query optimization, data replication, administration.

Advanced SQL skills (PL/SQL, TSQL).

Big Data processing Frameworks (PySpark).

Proficient in creating Azure Data Factory pipelines for copy activity and custom data pipelines in Databricks with CI CD (Azure DevOPS Git Integration) .

Data migration and data integration across different platforms Oracle to Azure Data Lake, Oracle to Databricks, Oracle to Microsoft Fabric.

Desirable Skills and Experience:

Data lake design and development with focus on extracting, transforming data coming from various data sources and loading into Lakehouse in medallion architecture.

Advanced skills and hands-on experience with Oracle databases, Azure SQL Server and Azure Data Factory.

Cloud technologies (Azure, Google, AWS).

PL/SQL for data extraction, transformation, and loading, initial setup and other ETL experience management and support, such as troubleshooting, performance tuning, failover and recovery.

Scripting languages (Python, Unix shell, Scala).

Supplier Comments Req Closing Date/Time 2026-10-21, 10:00 a.m.

Max number of candidate submission – 1(one)

Candidate is required to come in office 5 days a week

Must haves:

Design, build, automate and optimize complex data ETL/ELT processes using Databricks, Delta Live Tables (DLT) and equivalent.

Development of unified data platforms using Databricks and Unity catalog or its equivalent.

Relational databases (Oracle, MySQL, SQL Server), data modelling (relational & dimensional), advanced SQL, query optimization, data replication, administration.

Advanced SQL skills (PL/SQL, TSQL).

Big Data processing Frameworks (PySpark).

Proficient in creating Azure Data Factory pipelines for copy activity and custom data pipelines in Databricks with CI CD (Azure DevOPS Git Integration) .

Data migration and data integration across different platforms Oracle to Azure Data Lake, Oracle to Databricks, Oracle to Microsoft Fabric.