Cloud Data Engineer

at Intuitive Technology Partners

DP
Posted by shalu Jain
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Remote only
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9 - 20 yrs
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Best in industry
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Full time
Skills
OLTP
data ops
cloud data
Amazon Web Services (AWS)
Google Cloud Platform (GCP)
Windows Azure
PySpark
ETL
Scala
CI/CD
Data-flow analysis

THE ROLE:Sr. Cloud Data Infrastructure Engineer

As a Sr. Cloud Data Infrastructure Engineer with Intuitive, you will be responsible for building or converting legacy data pipelines from legacy environments to modern cloud environments to help the analytics and data science initiatives across our enterprise customers. You will be working closely with SMEs in Data Engineering and Cloud Engineering, to create solutions and extend Intuitive's DataOps Engineering Projects and Initiatives. The Sr. Cloud Data Infrastructure Engineer will be a central critical role for establishing the DataOps/DataX data logistics and management for building data pipelines, enforcing best practices, ownership for building complex and performant Data Lake Environments, work closely with Cloud Infrastructure Architects and DevSecOps automation teams. The Sr. Cloud Data Infrastructure Engineer is the main point of contact for all things related to DataLake formation and data at scale. In this role, we expect our DataOps leaders to be obsessed with data and providing insights to help our end customers.

ROLES & RESPONSIBILITIES:

  • Design, develop, implement, and tune large-scale distributed systems and pipelines that process large volume of data; focusing on scalability, low-latency, and fault-tolerance in every system built
  • Developing scalable and re-usable frameworks for ingesting large data from multiple sources.
  • Modern Data Orchestration engineering - query tuning, performance tuning, troubleshooting, and debugging big data solutions.
  • Provides technical leadership, fosters a team environment, and provides mentorship and feedback to technical resources.
  • Deep understanding of ETL/ELT design methodologies, patterns, personas, strategy, and tactics for complex data transformations.
  • Data processing/transformation using various technologies such as spark and cloud Services.
  • Understand current data engineering pipelines using legacy SAS tools and convert to modern pipelines.

 

Data Infrastructure Engineer Strategy Objectives: End to End Strategy

Define how data is acquired, stored, processed, distributed, and consumed.
Collaboration and Shared responsibility across disciplines as partners in delivery for progressing our maturity model in the End-to-End Data practice.

  • Understanding and experience with modern cloud data orchestration and engineering for one or more of the following cloud providers - AWS, Azure, GCP.
  • Leading multiple engagements to design and develop data logistic patterns to support data solutions using data modeling techniques (such as file based, normalized or denormalized, star schemas, schema on read, Vault data model, graphs) for mixed workloads, such as OLTP, OLAP, streaming using any formats (structured, semi-structured, unstructured).
  • Applying leadership and proven experience with architecting and designing data implementation patterns and engineered solutions using native cloud capabilities that span data ingestion & integration (ingress and egress), data storage (raw & cleansed), data prep & processing, master & reference data management, data virtualization & semantic layer, data consumption & visualization.
  • Implementing cloud data solutions in the context of business applications, cost optimization, client's strategic needs and future growth goals as it relates to becoming a 'data driven' organization.
  • Applying and creating leading practices that support high availability, scalable, process and storage intensive solutions architectures to data integration/migration, analytics and insights, AI, and ML requirements.
  • Applying leadership and review to create high quality detailed documentation related to cloud data Engineering.
  • Understanding of one or more is a big plus -CI/CD, cloud devops, containers (Kubernetes/Docker, etc.), Python/PySpark/JavaScript.
  • Implementing cloud data orchestration and data integration patterns (AWS Glue, Azure Data Factory, Event Hub, Databricks, etc.), storage and processing (Redshift, Azure Synapse, BigQuery, Snowflake)
  • Possessing a certification(s) in one of the following is a big plus - AWS/Azure/GCP data engineering, and Migration.

 

 

KEY REQUIREMENTS:

  • 10+ years’ experience as data engineer.
  • Must have 5+ Years in implementing data engineering solutions with multiple cloud providers and toolsets.
  • This is hands on role building data pipelines using Cloud Native and Partner Solutions. Hands-on technical experience with Data at Scale.
  • Must have deep expertise in one of the programming languages for data processes (Python, Scala). Experience with Python, PySpark, Hadoop, Hive and/or Spark to write data pipelines and data processing layers.
  • Must have worked with multiple database technologies and patterns. Good SQL experience for writing complex SQL transformation.
  • Performance Tuning of Spark SQL running on S3/Data Lake/Delta Lake/ storage and Strong Knowledge on Databricks and Cluster Configurations.
  • Nice to have Databricks administration including security and infrastructure features of Databricks.
  • Experience with Development Tools for CI/CD, Unit and Integration testing, Automation and Orchestration
Read more

About Intuitive Technology Partners

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Aakriti Gupta
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Shwetangi Goyal
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Dhinesh Raja
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shalu Jain
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