DATA PLATFORM ENGINEER SKILLS, EXPERIENCE, AND JOB REQUIREMENTS

Published: October 7, 2024 - The Data Platform Engineer develops scalable, and distributes production systems focused on personalization and recommendation technologies. This position demonstrates profound coding prowess and design acumen, alongside a track record of autonomous project ownership and solution-oriented global thinking for significant business impacts. This role possesses deep expertise in a variety of AWS Cloud Services and big data tools, complemented by capabilities in serverless data processing frameworks, deployment technologies, No-SQL databases, and UX development for enhancing data scientist workflows.

Essential Hard and Soft Skills for a Standout Data Platform Engineer Resume
  • Data Architecture
  • Cloud Computing
  • SQL Programming
  • ETL Development
  • Data Warehousing
  • Data Modeling
  • Python
  • Data Security
  • Big Data Technologies
  • Performance Tuning.
  • Problem Solving
  • Communication
  • Team Collaboration
  • Adaptability
  • Time Management
  • Critical Thinking
  • Attention to Detail
  • Project Management
  • Leadership
  • Stakeholder Engagement.

Summary of Data Platform Engineer Knowledge and Qualifications on Resume

1. BS in Computer Science with 5 years of Experience

  • Demonstrate a range of experience with Data and Analytics technologies and concepts.
  • Strong technical skills, with experience researching, implementing, and onboarding new technologies.
  • Experience working with cloud technologies and solutions, with preferred depth in AWS services.
  • Ability to successfully execute proof of concept projects and implement new technologies.
  • Understanding of new technology and trends including database and analytics technologies, data science, AI and machine learning.
  • Creative and innovative mindset with the ability to drive to outcomes.
  • Understanding complex technology solutions and how they interrelate with other technologies and business drivers across the enterprise.
  • Ability to work with ambiguity and organize requirements to identify options.
  • Ability to build productive relationships and collaborate with partners and stakeholders.
  • Ability to perform grasping tasks throughout the entire work day (examples: handwriting, grasping of equipment/machines, paper manipulation, sorting, folding, handling stacks of paper)

2. BS in Data Science with 2 years of Experience

  • Experience working in building internal data platforms, or heavily using internal or third-party data platforms and tooling.
  • Strong understanding of modern data processing paradigms and tooling, OLTP & OLAP database fundamentals.
  • Strong proficiency in one or more programming languages frequently used in a modern data stack, such as Python, Scala, and/or SQL.
  • Solid understanding of common data engineering and integration tasks (data ingestion & validation, clickstream data processing, data warehousing, et al).
  • Experience with any of Airflow, Spark, Scala, Beam, Parquet
  • Expertise in SQL for analytics/reporting/business intelligence, including basic comprehension of query execution plans, and query tuning/optimization for Google BigQuery, Amazon Redshift, Snowflake, or similar.
  • Experience working with semi- or unstructured data in a data lake.
  • Experience working in data engineering or a similar discipline at an online marketplace or similar consumer technology company.
  • Coding skills in Python and/or Go
  • Experience designing public APIs (in a language and/or REST)
  • Experience and comfort building everything on top of AWS
  • Experience with Linux, and Docker

3. BS in Data Science with 3 years of Experience

  • Experience with the development and maintenance of build scripts and the use of Jenkins for build automation.
  • Experience with Python development
  • Ansible/Chef/Puppet or similar deployment scripting language
  • Experience in unit test automation
  • Experience in continuous integration/deployment and development techniques
  • Exposure to Scrum/Agile delivery methodologies
  • A solid grounding in Hadoop Big Data technologies such as HDFS, YARN, SQOOP, Hive, Impala and general cluster management (AWS)
  • Experience with Cloud (AWS) application and infrastructure services such as VPC, EC2, S3, IAM
  • Experience with SQL skills for data analysis covering both Oracle and MS SQL Server.
  • Ability to use MS Excel / Word / Visio / Wiki for documentation of architecture/design/implementation.
  • Familiarity with ETL concepts and solutions.
  • Familiarity with scheduling and orchestration tools.
  • Familiarity with data extraction from 3rd party APIs.

4. BA in Software Engineering with 5 years of experience

  • Strong experience in building scalable distributed and production systems, specifically around personalization and recommendation systems.
  • Exceptional coding and design skills
  • Experience working autonomously and taking ownership of projects.
  • Ability to think globally, devising and building solutions that meet many needs and prioritize business impact
  • Strong cross-functional communication skills that help simplify and move complex problems forward with business partners
  • Expertise in various AWS Cloud Services like AWS S3, SNS, SQS, EMR, CloudFormation, DynanmoDB, CodePipeline, Lambda, IAM, EC2 etc.
  • Experience in developing Serverless Data processing Frameworks using AWS Step Functions, Fargate, S3, SQS Glue and Athena.
  • Experience working with big data tools such as Hadoop, Spark, Spark-SQL, Spark-Streaming, Kafka, Hive, Impala, Sqoop, Map-Reduce, etc.
  • Knowledge of Deployment tools and technologies like Terraform, Ansible, Docker, Puppet and Jenkins
  • Experience in No-SQL databases like Casandra and Mongo-DB
  • Some UX development exposure and understanding the spectrum of personal workflows of data scientists
  • Experience writing technical documentation and teaching people how to use the tools