Data Engineer, Data Platform - USDS

Tiktok•Seattle, WA

58d•Hybrid

About The Position

The Data Engineering team in Data Platform USDS is focused on ensuring the stability, reliability, scalability and risk management of TikTok's US data processing ecosystem. We maintain and operate both batch and streaming pipelines for multiple vertical businesses such as TikTok and TikTok Shop while simultaneously adhering to strict data compliance standards. We collaborate with a host of internal teams to better enable user experiences by delivering high quality at scale. As a data engineer in the data platform team area, you will have the opportunity to build, optimize and grow one of the largest data platforms in the world that directly supports the TikTok app. You'll have the opportunity to gain hands-on experience on all kinds of systems in the data platform ecosystem. Your work will have a direct and huge impact on the company's core products as well as hundreds of millions of users. In order to enhance collaboration and cross-functional partnerships, among other things, at this time, our organization follows a hybrid work schedule that requires employees to work in the office 3 days a week, or as directed by their manager/department. We regularly review our hybrid work model, and the specific requirements may change at any time.

Requirements

Bachelors degree in Statistics, Computer Science or a related technical field involving software or systems engineering, or equivalent practical experience.
2+ years of experience working with data analytics and data engineering, including experience with data cleaning and preprocessing, data ingestion and ETL(Extraction, Transformation & Loading), data analysis and dashboard development.
2+ years experience building dashboards in Tableau, Power BI or any similar visualization tool.

Nice To Haves

Proficiency in distributed data processing using Big Data technologies like Spark/Scala, Python, Hadoop/HDFS/AWS/S3 and Kafka.
Proficiency in data modeling, data design, SQL and databases.
3+ years of Experience with Big Data technologies (Hadoop, M/R, Hive, Spark, Metastore, Presto, Flume, Kafka, ClickHouse, Flink etc).
Strong background in algorithms and data structures.
Experience working with PII and GDPR data.
Ability to communicate effectively, both written and verbal, with technical and non-technical partners.
Ability to deliver consistent high quality results while working in a fast paced environment.
Passionate, curious, and seeking to tackle every day problems with innovation.

Responsibilities

Design, implement and maintain reliable, scalable, robust and extensible big data systems that support core products and business.
Design, build, and maintain data pipelines utilizing optimal ETL patterns, frameworks, query techniques, sourcing from structured and unstructured data sources to ensure data is easily accessible and can be used effectively by other members of the organization.
Extract data from various sources such as APIs, HIVE tables and other structured and unstructured data sources to process and store large volumes of data ensuring data accuracy, consistency, and security.
Implement and monitor quality control measures to ensure data accuracy, completeness, and consistency.
Create and maintain technical documentation, such as data dictionaries, data flow diagrams, and system documentation, to ensure efficient and effective data management and analysis.
Optimize pipelines, dashboards, frameworks, and systems to facilitate easier development of data artifacts.
Collaborate with engineers, product managers, and data scientists to understand data needs, representing key data insights in a meaningful way.
Ability to analyze and visualize data to provide business stakeholders with impactful, actionable insights.
Establish solid design and best engineering practice for engineers as well as non-technical people.

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