JOB DESCRIPTION

Why GM Financial Technology?

Innovation isn't just a talking point at GM Financial, it's how we operate. From generative AI and cloud-native technologies to peer-led learning and hackathons, our tech teams are building real solutions that make a difference. We're committed to AI-powered transformation, using advanced machine learning and automation to help us reimagine customer interactions and modernize operations, positioning GM Financial as a leader in digital innovation within a dynamic industry.

Join us and discover a workplace where your ideas matter, your development is prioritized, and you can truly make a global impact.

RESPONSIBILITIES

About the role:

We are expanding our efforts into complementary data technologies for decision support in areas of ingesting and processing large data sets including data commonly referred to as semi-structured or unstructured data. Our interests are in enabling data science and search-based applications on large and low latent data sets in both a batch and streaming context for processing. To that end, this role will engage with team counterparts in exploring and deploying technologies for creating data sets using a combination of batch and streaming transformation processes. These data sets support both off-line and in-line machine learning training and model execution. Other data sets support search engine-based analytics. Exploration and deployment of technologies activities include identifying opportunities that impact business strategy, collaborating on the selection of data solutions software, and contributing to the identification of hardware requirements based on business requirements. Responsibility also includes coding, testing, and documentation of new or modified scalable analytic data systems including automation for deployment and monitoring. This role partakes, along with team counterparts, in developing solutions in an end-to-end framework on a group of core data technologies.

In this role you will:

Contribute to the evaluation, research, experimentation efforts with batch and streaming data engineering technologies in a lab to keep pace with industry innovation
Work with data engineering related groups to inform on and showcase capabilities of emerging technologies and to enable the adoption of these new technologies and associated techniques
Contribute to the definition and refinement of processes and procedures for the data engineering practice
Work closely with data scientists, data architects, ETL developers, other IT counterparts, and business partners to identify, capture, collect and format data from the external sources, internal systems, and the data warehouse to extract features of interest
Code, test, deploy, monitor, document and troubleshoot data engineering processing and associated automation

QUALIFICATIONS

What makes you an ideal candidate?

Experience with Adobe solutions (ideally Adobe Experience Platform, XDM, RTCDP DTM/Launch) and REST APIs
Digital technology solutions (DMPs, CDPs, Tag Management Platforms, Cross-Device Tracking, SDKs, etc.) Knowledge of Real Time-CDP and Journey Analytics solution
SQL experience: querying data and sharing what insights can be derived
Working knowledge of Agile development /SAFe, Scrum and Application Lifecycle Management.
Experience with ingesting various source data formats such as JSON, Parquet, SequenceFile, Cloud Databases, MQ, Relational Databases such as Oracle.
Experience with Cloud technologies (such as Azure, AWS, GCP) and native toolsets.
Understanding of cloud computing technologies, business drivers and emerging computing trends.
Thorough understanding of Hybrid Cloud Computing: virtualization technologies, Infrastructure as a Service, Platform as a Service and Software as a Service Cloud delivery models and the current competitive landscape.
Working knowledge of Object Storage technologies to include but not limited to Data Lake Storage Gen2, S3, Minio, Ceph, ADLS etc.
Strong background with source control management systems; Build Systems (Maven, Gradle, Webpack); Code Quality (Sonar); Artifact Repository Managers (Artifactory), Continuous Integration/ Continuous Deployment (Azure DevOps).
Experience with processing large data sets using Hadoop, HDFS, Spark, Kafka, Flume or similar distributed systems.
Experience with NoSQL data stores such as CosmosDB, MongoDB, Cassandra, Redis, Riak or other technologies that embed NoSQL with search such as MarkLogic or Lily Enterprise.
Creating and maintaining ETL processes
Knowledgeable of best practices in information technology governance and privacy compliance
Troubleshoot complex problems and work across teams to meet commitments.
Excellent computer skills and proficiency in digital data collection.
Ability to work in an Agile/Scrum team environment
Strong interpersonal, verbal, and writing skills.
Understanding of big data platforms and architectures, data stream processing pipeline/platform, data lake and data lake houses
Understanding of cloud solutions such as Google Cloud Platform, Microsoft Azure & Amazon AWS cloud architecture & services
Understanding of GDPR, privacy & security topics
Strong in the use of Microsoft Office software, data querying platforms (Databricks is a plus) and statistical programming tools such as Python

Experience

2-4 years of hands-on experience with data engineering required
Bachelor's degree in related field or equivalent experience required

What We Offer: Generous benefits package available on day one to include: 401K matching, bonding leave for new parents (12 weeks, 100% paid), tuition assistance, training, GM employee auto discount, community service pay and nine company holidays.

Our Culture: Our team members define and shape our culture - an environment that welcomes innovative ideas, fosters integrity, and creates a sense of community and belonging. Here we do more than work - we thrive.

Compensation: Competitive pay and bonus eligibility

Work Life Balance: Flexible hybrid work environment, 2 days a week in office in Irving, TX

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