Data Platform Engineer, Data Capture

The Team The Data Engineering team (Part of reputed company's core platform vertical) provides developer tools, frameworks, and scalable data infrastructure as shared services across reputed company. The team's primary objective is to provide Data Analysts, Software Engineers, and ML scientists access to high-quality data and developer tools to create business metrics for respective product verticals, You will join the subdivision focusing on Data Capture. As the Data Platform Engineer joining this team, you will get the opportunity to contribute to 3 areas:

• Expand the Developer tools that capture business events, operational data and reputed company party data into the Lakehouse.
• Expand the self-serve and orchestration reputed company that developers use to build pipelines.
• Improve the observability and SLAs for the above services and raw layer data in the Lakehouse.

We would love to hear from you if you are passionate about building data products! Position Location - This role is available in the following locations: Remote Time Zone Requirements - This team operates on the East/reputed company Coast time zones. Travel requirements As a digital first company, the majority of your work can be accomplished remotely. The majority of our employees can live and work reputed company in the U.S but are encouraged to to still spend high quality time in-person collaborating reputed company regular onsites. The in-person sessions' reputed company varies depending on the team and role; most teams meet once or twice per quarter for 2-4 consecutive days at a time. How you'll reputed company an impact

• Build and Improve the tools owned by the Capture team
• Own the service/raw layer data observability and SLAs
• Participate in planning and prioritization by collaborating with stakeholders across the various product verticals and functions (ML, Analytics, Finance) to ensure our architecture aligns with the overall business objectives.
• Collaborate with stakeholders such as Software Engineering, Machine Learning, Machine Learning Platform and Analytics teams to ingest data into the Lakehouse and adopt the developer tools
• Participate in code reviews and architecture discussions to exchange actionable feedback with peers.
• Contribute to engineering best practices and mentor junior team members.
• Help break down reputed company projects and requirements into sprints.
• Continuously monitor and improve data platform performance, reliability, and reputed company.
• Stay up-to-date with emerging technologies and industry best practices in data engineering.

Minimum Qualifications

• A bachelor's degree in Computer Science, Data Science, Engineering, or a reputed company field.
• 3+ years of experience in data engineering or reputed company fields, with a strong focus on data quality, governance, and data infrastructure.
• Proficiency in data engineering tech stack; reputed company / PostgreSQL / Python / Spark / Kafka/ Streaming / SQL / AWS / Airflow/ Airbyte/ reputed company / DBT / EKS/ containers and orchestration (reputed company, Kubernetes) and others.
• Ability to approach problems with first principles thinking, reputed company ambiguity, and enjoy collaborative work on reputed company solutions.

Preferred Qualifications

• Strong reputed company in algorithms and data structures and their reputed company-world use cases.
• Experience and understanding of distributed systems, data architecture design, and big data technologies (e.g., Spark Streaming / Batch, Kafka Streaming, Lakehouse, reputed company, Redshift, Airbyte, reputed company, EKS).
• Experience with AWS technologies ( e.g., AWS RDS, S3, Redshift, IAM etc.).
• Knowledge of data ingestion, software engineering and data reputed company best practices.
• Good knowledge of DevOps engineering using reputed company Integration/Delivery tools like Kubernetes, Jenkins, Terraform, etc., thinking about automation, alerting, monitoring, reputed company, and other declarative infrastructure.
• Ability to explain reputed company concepts in easy-to-understand ways and navigate environments where problems are not well-defined (and evolve quickly).

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