layiq

/la·yiq/adjective

worthy; deserving; fitting; suitable.

A role, opportunity, or path that merits attention, time, and pursuit.

LAYIQ - Senior Data Engineer at General Motors

Assemble large, complex data sets that meet functional and non-functional business requirements.
Identify, design, and implement process improvements, including automation, data delivery optimization, and infrastructure redesign for scalability.
Lead and deliver data-driven solutions across multiple languages, tools, and technologies.
Develop and configure end-to-end data pipelines using Fivetran, ensuring efficient extraction, transformation, and loading (ETL) processes.
Integration Setup: Connect multiple data sources (APIs, SaaS platforms, databases) to Fivetran and manage schema mapping and transformations.
Monitor pipeline performance, troubleshoot issues, and implement improvements for scalability and reliability.
Contribute to architecture discussions, solution design, and strategic technology adoption.
Build and optimize highly scalable data pipelines incorporating complex transformations and efficient code.
Design and develop new source system integrations from varied formats (files, database extracts, APIs).
Design and implement solutions for delivering data that meets SLA requirements.
Work with operations teams to resolve production issues related to the platform.
Apply best practices such as Agile methodologies, design thinking, and continuous deployment.
Develop tooling and automation to make deployments and production monitoring more repeatable.
Collaborate with business and technology partners, providing leadership, best practices, and coaching.
Mentor peers and junior engineers; educate colleagues on emerging industry trends and technologies.
Your Skills & Abilities (Required Qualifications)
Bachelor’s degree in Computer Science, Software Engineering, or related field, or equivalent experience
7+ years of data engineering/development experience, including Python or Scala, SQL, and relational/non-relational data storage. (ETL frameworks, big data processing, NoSQL)
3+ years of experience in distributed data processing (Spark) and container orchestration (Kubernetes)
Proficiency in data streaming in Kubernetes and Kafka
Proven experience with Fivetran (pipeline setup, connectors, transformations)
Experience with cloud platforms – Azure preferred; AWS or GCP also considered.
Solid understanding of CI/CD principles and tools
Familiarity with big data technologies such as Hadoop, Hive, HBase, Object Storage (ADLS/S3), Event Queues.
Strong understanding of performance optimization techniques such as partitioning, clustering, and caching
Proficiency with SQL, key-value datastores, and document stores
Familiarity with data architecture and modeling concepts to support efficient data consumption
Advanced understanding of data normalization and denormalization techniques.
Translate enterprise requirements into effective data models and address project requirements when applicable.
Design, build, and optimize scalable batch and streaming data pipelines using Databricks (Apache Spark, Delta Lake) to support Medalion Architecture.
Automate workflows and maintain clear documentation for all data processes.
Contribute to the design and operational management of a cloud-native data platform on Azure , integrating services such as Azure Data Lake Storage, Event Hubs, Azure SQL, and AKS.
Monitor and troubleshoot data pipelines and platform workloads. Optimize Spark jobs, cluster configurations, and SQL warehouses to improve performance
Strong collaboration and communication skills; ability to work across multiple teams and disciplines.

Senior Data Engineer

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