- Work with data to solve business problems by building and maintaining the infrastructure to answer questions and improve processes
- Streamline data science workflows, adding value to product offerings and building out customer lifecycle and retention models
- Develop and construct data products and services, integrating them into systems and business processes
- Transform raw data into usable information for data scientists and business analysts to interpret
- Make data accessible so that organizations can use it to evaluate and optimize their performance
- Be an advocate for best practices and continued learning in data engineering
- Champion data engineering across the organization to drive data-driven decision making
Objectives
Responsibilities
- Design, build, and maintain scalable data pipelines and build out new API integrations to support increasing data volume and complexity
- Develop algorithms to transform data into useful, actionable information
- Implement data flows to connect operational systems, data for analytics, and business intelligence systems
- Build, test, and maintain database pipeline architectures
- Create and implement processes and systems to monitor data quality, ensuring production data is always accurate and available
- Collaborate with data science and business intelligence teams to develop data models and pipelines for research, reporting, and machine learning
- Build data pipelines that clean, transform, and aggregate data from disparate sources
- Write ETL (extract, transform, load) scripts and code to ensure optimal performance
- Develop and maintain infrastructure using AWS and SQL technologies for effective data extraction, transformation, and loading
- Model front-end and back-end data sources to enable comprehensive data analysis
- Build analytical tools that provide practical understanding of business performance indicators
- Write unit and integration tests, contribute to engineering documentation
- Perform data analysis to troubleshoot data-related issues and assist in resolution
- Ensure compliance with data governance and security policies
- Automate manual data flows to enable scaling and repeatable use
- Remain up-to-date with developments in technology and industry standards
Required Skills & Qualifications
- Bachelor's degree in computer science, data engineering, information technology, engineering, or related discipline
- Three or more years of experience with Python, SQL, and data visualization/exploration tools
- Proficiency in programming languages such as Python, Java, SQL, and Scala
- Knowledge of big data tools including Hadoop, Spark, Kafka, and MongoDB
- Familiarity with AWS ecosystem, specifically Redshift, RDS, EMR, and EC2
- Understanding of database systems and data warehousing concepts
- Experience with ETL processes and tools
- Knowledge of data modeling, data mining, and segmentation techniques
- Strong understanding of batch and streaming data processing techniques
- Excellent analytical and problem-solving skills
- Communication skills, especially for explaining technical concepts to nontechnical business leaders
- Ability to work on a dynamic, research-oriented team with concurrent projects
- Understanding of data lifecycle management including data collection, access, use, storage, transfer, and deletion
Preferred Skills & Qualifications
- Master's degree in computer science, data engineering, or related technical field
- Experience in building or maintaining ETL processes at scale
- Professional certification such as Google Certified Professional Data Engineer, IBM Certified Data Engineer, or Cloudera CCP Data Engineer
- Familiarity with Agile software development methodologies
- Experience with cloud computing tools such as AWS, Azure, and Google Cloud Platform
- Knowledge of NoSQL databases
- Experience with data streaming systems like Spark-Streaming, Storm, or Kafka
- Proficiency with pipeline orchestration tools like Apache Airflow, Luigi, or Azkaban
- Experience with data lakes, Delta Lake, and Hive
- Knowledge of machine learning concepts and algorithms
- Experience with containerization technologies like Docker and Kubernetes
- Familiarity with data visualization tools such as Tableau or Power BI
- Understanding of cybersecurity and data protection principles
- Experience working in distributed environments with global teams
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What Does a Data Engineer Do?
A data engineer designs, builds, and maintains scalable systems and data pipelines that enable organizations to collect, store, and process large volumes of data from diverse sources to make it accessible for analysis and decision-making. They work at the foundation of the data processing chain, creating the infrastructure that transforms raw data into usable information for data scientists and business analysts.
Organizations need data engineers because they ensure that reliable, high-quality data flows smoothly from its source to its destination efficiently and securely. Data engineers work across departments, collaborating closely with data science teams, business intelligence teams, and IT professionals to develop data models and pipelines that support research, reporting, machine learning, and strategic business decisions.
A data engineer needs technical expertise in programming languages like Python, SQL, Java, and Scala, along with proficiency in big data technologies such as Hadoop, Spark, and Kafka. They must understand database systems, ETL processes, cloud computing platforms, and data warehousing concepts, combined with strong problem-solving abilities and the capacity to communicate complex technical concepts to non-technical stakeholders.
What Are the Responsibilities of a Data Engineer?
The responsibilities of a data engineer are to design and build data pipelines, ensure data quality and accessibility, and create infrastructure that supports data-driven decision making across the organization. They develop algorithms to transform raw data into actionable insights and implement systems that monitor data quality to ensure accuracy.
Data engineer duties include building scalable data pipelines and API integrations, writing ETL scripts to optimize data processing, implementing data flows between operational systems and analytics platforms, and maintaining database architectures. They also collaborate with analytics and business teams to improve data models, automate manual data processes, and ensure compliance with data governance and security policies.
Understanding these comprehensive responsibilities helps organizations ask relevant interview questions that identify candidates who can effectively manage complex data infrastructure, collaborate across teams, and deliver the robust data systems essential for business success.
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