Job brief
We are seeking a highly skilled Data Engineer to join our growing data platform team and help us build the next generation of our analytics infrastructure. In this role, you will be responsible for architecting scalable data models, automating data workflows, and ensuring our stakeholders have reliable, real-time access to business-critical information. You will work with a modern tech stack including AWS, Snowflake, and Airflow to turn complex, high-velocity data into a strategic asset. If you are passionate about data architecture and thrive in a collaborative, problem-solving environment, we would love to hear from you.
Key highlights
- Design, build, and maintain scalable ETL/ELT data pipelines using tools like Apache Airflow, dbt, or AWS Glue to move data between heterogeneous sources.
- Architect and manage high-performance data warehouses and data lakes, such as Snowflake, BigQuery, or Redshift, to ensure optimal query performance and cost-efficiency.
- Implement streaming data solutions utilizing Apache Kafka or Amazon Kinesis to support real-time analytical requirements and low-latency feature engineering.
- Collaborate with data scientists and analysts to model complex datasets, ensuring high-quality, normalized, and easily consumable data for downstream reporting platforms.
What is a Data Engineer?
A Data Engineer is a specialized software engineer focused on the architecture, construction, and maintenance of the systems that collect, store, and process massive datasets. By designing robust data pipelines and integrating disparate sources, the Data Engineer ensures that information is accurate, accessible, and ready for analysis by data scientists and business intelligence teams. Their work leverages cloud-native technologies, distributed computing frameworks, and sophisticated data modeling techniques to support the entire data lifecycle of a modern organization.
What does a Data Engineer do?
A Data Engineer spends their time building and optimizing ETL/ELT pipelines to move and transform data from transactional databases into scalable data warehouses or data lakes. They write complex SQL queries, develop automation scripts in Python or Scala, and configure streaming services like Apache Kafka to handle real-time data ingestion. Collaboration is constant; they work alongside DevOps engineers to ensure pipeline reliability, provide clean datasets for analytics teams, and participate in code reviews to maintain high data quality standards across the infrastructure.
Key responsibilities
- Design, build, and maintain scalable ETL/ELT data pipelines using tools like Apache Airflow, dbt, or AWS Glue to move data between heterogeneous sources.
- Architect and manage high-performance data warehouses and data lakes, such as Snowflake, BigQuery, or Redshift, to ensure optimal query performance and cost-efficiency.
- Implement streaming data solutions utilizing Apache Kafka or Amazon Kinesis to support real-time analytical requirements and low-latency feature engineering.
- Collaborate with data scientists and analysts to model complex datasets, ensuring high-quality, normalized, and easily consumable data for downstream reporting platforms.
- Write production-grade code in Python, Scala, or Java to automate data processing tasks, error handling, and monitoring within CI/CD pipelines.
- Optimize existing SQL queries and database schemas to reduce latency and infrastructure overhead in petabyte-scale production environments.
- Establish robust data governance, security, and privacy protocols to ensure compliance with industry standards like GDPR, HIPAA, or SOC2.
- Monitor data pipeline health using observability tools like Datadog or Prometheus, proactively resolving production incidents and performance bottlenecks.
Requirements and skills
- 3+ years of experience in data engineering, software development, or a related role with a focus on building distributed systems.
- Expert-level proficiency in SQL and advanced programming skills in Python, including libraries like Pandas, PySpark, or NumPy.
- Hands-on experience with cloud infrastructure services such as AWS (S3, EMR, Redshift) or Google Cloud Platform (GCS, Dataflow, BigQuery).
- Deep understanding of data modeling methodologies including Star Schema, Snowflake Schema, and normalization techniques for various data warehouse architectures.
- Familiarity with containerization and orchestration tools including Docker, Kubernetes, and orchestration engines like Apache Airflow.
- Proven experience with version control systems and CI/CD best practices using Git, GitHub Actions, or Jenkins to deploy data infrastructure.
- Bachelor’s degree in Computer Science, Data Engineering, or a related quantitative field, or equivalent practical industry experience.
- Professional certification such as AWS Certified Data Engineer, Google Professional Data Engineer, or similar industry-recognized credential.
FAQs
What does a Data Engineer do on a daily basis?
A Data Engineer is primarily responsible for maintaining the 'pipes' of a company’s data infrastructure. On a daily basis, they develop and test ETL/ELT pipelines, troubleshoot data quality issues, optimize SQL database performance, and work with cloud platforms like AWS or Azure. They also interact with stakeholders to understand data requirements and ensure that the architecture can support the organization's evolving analytical and machine learning needs.
What are the essential skills for a Data Engineer?
The most important skills for a Data Engineer include advanced SQL mastery, programming fluency in Python or Scala, and a deep understanding of distributed systems like Spark or Hadoop. Additionally, candidates must be comfortable working with cloud-based data warehouses (e.g., Snowflake, BigQuery) and have experience in pipeline orchestration. Understanding data modeling, database design principles, and basic cloud security are also fundamental to the role.
How is a Data Engineer different from a Data Scientist?
While a Data Scientist focuses on analyzing data, building predictive models, and extracting actionable business insights, a Data Engineer focuses on the foundational infrastructure that makes that analysis possible. A Data Engineer builds the system that collects, cleans, and stores the data reliably so the Data Scientist can access it. In short, the Data Engineer provides the 'raw material' and the Data Scientist provides the 'final product'.
Why is the role of a Data Engineer important to an organization?
Data is one of the most valuable assets in a modern organization, but it is often siloed, messy, or inaccessible. A Data Engineer is critical because they turn unorganized, raw data into a structured, reliable, and accessible format that allows leadership to make data-driven decisions. By ensuring data integrity and pipeline reliability, they directly enable every department—from marketing to product—to operate more efficiently and innovate faster.