Agility Partners LLC

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 M. 3+ of hands on experience in building metadata driven data pipelines using Azure Data Factory, Databricks / Spark for Cloud Datalake. Expert level SQL knowledge on databases such as but not limited to Snowflake, Netezza, Oracle, Sql Server, MySQL, Teradata. Experience or strong understanding of the 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)

data engineer

Agility Partners is looking for a Senior Data Management Engineer to build data pipelines, model and prepare data, perform complex data analysis to answer Business questions, build and automate data pipeline and quality framework to enable and promote self service data pipelines, assist in operationalizing the AI / ML Engineering solutions. This role is expected to lead and guide other team members and evangelize the design patterns as well as coding standards. This role plays an active part in our client's Data Modernization project to migrate the from on-prem platforms such as IBM Netezza to cloud. In this role, you will: 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 suchs 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 M
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. Benefits and Perks Fully remote!
Ability to join a collaborative team, making a positive impact on the financial stability and overall health of an organization
Culture-focused company focused on customer-first and a growth mindset
Great resume builder – gain experience with a Fortune 15 company
Medical, Dental and Vision plans (PPO and HSA plans available); Individual and Family coverage offerings
Long and short term disability coverage
401(k)
The Ideal Candidate 5+ years of experience in an Enterprise Data Management or Data Engineering role
3+ 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
3+ years of hands on experience on one or more of big data technologies such as Cloudera Hadoop, Pivotal, Vertica, MapR is a plus
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 the 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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