Data Ops Engineer
What you’ll do
Become part of an iconic brand that is set to revolutionize the electric pick-up truck & rugged SUV marketplace by achieving the following:
Contribute to the design, implementation, and maintenance of the overall cloud infrastructure data platform using modern IaC (Infrastructure as Code) practices.
Work closely with software development and systems teams to build Data Integration solutions.
Design and build Data models using tools such as Lucid, Talend, Erwin, MySQL workbench.
Define and enhance enterprise data model to reflect relationships and dependencies.
Review application data systems to ensure adherence to data governance policies.
Design and build ETL (Python), ELT(Python) infrastructure, automation, and solutions to transform data as required.
Design and Implement BI dashboards to visualize Trends and Forecasts.
Design and implement data infrastructure components, ensuring high availability, reliability, scalability, and performance.
Design, train and deploy ML models
Implement monitoring solutions to proactively identify and address potential issues.
Collaborate with security teams to ensure the data platform meets industry standards and compliance requirements.
Collaborate with cross-functional teams, including product managers, developers, and business partners to ensure robust and reliable systems.
What you’ll bring
We expect all employees to have integrity, curiosity, resourcefulness, and strive to exhibit a positive attitude, as well as a growth mindset. You’ll be comfortable with change and flexible in a fast-paced, high-growth environment. You’ll take a collaborative approach to achieve ambitious goals. Here's what else you'll bring:
Bachelor's degree in computer science, information technology, or related field or equivalent work experience.
5+ years of hands-on experience as DataOps Engineer in a manufacturing or automotive environment.
Experience with streaming and event-based architecture.
Proficient in building data pipelines using languages such as Python and SQL.
Experience with AWS based data services such as Glue, Kinesis, Firehose or other comparable services.
Experience with Structured, unstructured and time series databases.
Solid understanding of cloud data storage solutions such as RDS, DynamoDB, DocumentDB, Mongo, Cassandra, Influx.
Experience implementing data lakehouse solutions using Databricks.
Several years of experience working with cloud platforms such as AWS and Azure.
Experience with infrastructure as code (Terraform).
Proven ability to develop and deploy scalable ML models.
Hands-on experience in designing, training, and deploying ML models
Strong ability to extract actionable insights using ML techniques
Ability to leverage ML algorithms for forecasting trends and decision-making
Excellent problem-solving and troubleshooting skills. When a problem occurs, you run towards it not away.
Effective communication and collaboration skills. You treat colleagues with respect. You have a desire for clean implementations but are also humble in discussing alternative solutions and options.