Sr. Data Engineer

Project Description

Data Modernization project to migrate the from on-prem platforms such as IBM Netezza to cloud project


  • Team up with the engineering teams and enterprise architecture (EA) to define standards, design patterns, accelerators, development practices, DevOps and CI/CD automation
  • Create and maintain the data ingestion, quality testing and audit framework
  • Conduct complex data analysis to answer the queries from Business Users or Technology team partners either directly from Analysts or stemmed from one of the Reporting tools such PowerBI, Tableau, OBIEE.
  • Build and automate the data ingestion, transformation and aggregation pipelines using Azure
  • Data Factory, Databricks / Spark, Snowflake, Kafka as well as Enterprise Scheduler tools such as CA Workload automation or Control
  • Setup and evangelize the metadata driven approach to data pipelines to promote self service
  • Setup and continuously improve the data quality and audit monitoring as well as alerting
  • Constantly evaluate the process automation options and collaborate with engineering as well as architecture to review the proposed design.
  • Demonstrate mastery of build and release engineering principles and methodologies including source control, branch management, build and smoke testing, archiving and retention practices
  • Adhere to and enhance and document the design principles best practices by collaborating with Solution and in some cases Enterprise Architects
  • Participate in and support the Data Academy and Data Literacy program to train the Business Users and Technology teams on Data
  • Respond SLA driven production data quality or pipeline issues
  • Work in a fast-paced Agile/Scrum environment
  • Identify and assist with implementation of DevOps practices in support of fully automated deployments
  • Document the Data Flow Diagrams, Data Models, Technical Data Mapping and Production Support Information for Data Pipelines
  • Follow the Industry-standard data security practices and evangelize the same across the team.


  • 5+ years of experience in an Enterprise Data Management or Data Engineering role
  • 3+ years of hands-on experience in building metadata driven data pipelines using Azure Data Factory, Databricks / Spark for Cloud Datalake
  • 5+ years hands on experience with using one or more of the following for data analysis and wrangling Databricks, Python / PySpark, Jupyter Notebooks
  • Expert level SQL knowledge on databases such as but not limited to Snowflake, Netezza, Oracle, Sql Server, MySQL, Teradata
  • Experience working in a multi-developer environment and hands-on experience in using either azure devops or gitlab
  • Preferably experienced in SLA driven Production Data Pipeline or Quality support
  • Experience or strong understanding of traditional enterprise ETL platforms such as IBM Datastage, Informatica, Pentaho, Ab Initio etc.
  • Functional knowledge of some of the following technologies - Terraform, Azure CLI, PowerShell, Containerization (Kubernetes, Docker)
  • Functional knowledge of one or more Reporting tools such as PowerBI, Tableau, OBIEE
  • Team player with excellent communication skills, ability to communicate with the customer directly and able to explain the status of the deliverables in scrum calls
  • Ability to implement Agile methodologies and work in an Agile DevOps environment


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