Do you ever wonder what happens inside the cloud?
Based in New York and Cambridge, DigitalOcean is a dynamic, high-growth technology company that serves a robust and passionate community of developers around the world. Our mission is to simplify cloud computing for every developer. We are working on solving some of the most challenging and interesting technology projects around, on a scale unmatched by most.
We want people who are passionate about building features that you and your peers will love.
Our Business Operations unit interfaces with the rest of DigitalOcean to ensure our ability to execute on our product vision and support our growing customer base in a data-driven and efficient manner. As a Data Engineer, you will join a growing Data & Analytics team that partners with decision-makers across the organization to catalyze business growth by building and scaling our company-wide data platform providing robust and relevant analysis, insights and data products.
The mission of Data Engineering is to effectively transform large amounts of data from various sources (structured/unstructured/streaming) into a comprehensive, unified data environment. This data environment is critical for decision-makers in all aspects of the business for insights and intelligence, and also serves as the backbone for numerous data science products that are integrated in systems company-wide.
You should feel at home in a fast-paced startup environment, with the ability and desire to dive independently into incomplete and imperfect datasets. You should thrive in situations where decisions need to be made as quickly and effectively as possible based on the available data, and where your code, insights and advice are used daily to make decisions that affect over a million users. You should be motivated by opportunities to create solutions where one does not already exist.
What You’ll Be Doing:
- Join a team of Data Engineers, Data Scientists, and Product Analysts to develop holistic solutions to pressing business questions.
- Contribute to the vision and architecture of DigitalOcean’s data pipeline framework and environment to accommodate our scaling global business.
- Develop and implement data acquisition and transformation processes for a variety of data sources, including evaluating new technologies and tuning performance.
- Focus on production status and data quality of the data environment and data products being delivered to the business, and effectively communicate to internal user base regarding production changes/issues.
- Interface closely with data infrastructure, engineering and technical operations teams to ensure reliability and scalability of ETL framework and data environment.
What We’ll Expect From You:
- Track record of developing and evolving complex data environments and intelligence platforms for business users
- Significant experience in custom ETL design, implementation and maintenance, and hands-on experience with schema design and dimensional data modeling
- Significant experience working large scale data processing and integration, both in batch and streaming, using open source tools such as Hadoop, Hive, Airflow, Kafka, and Spark
- Has the ability to build and create the prototypes for the data solutions, from ad-hoc SQL scripts to exposing data via APIs.
- Experienced with multiple scripting languages, including Python, R, or Perl, and have a deep understanding of MapReduce and expertise big data tools such as Pig, Hive, and Spark.
- Demonstrable ability to relate high-level business requirements to technical ETL and data infrastructure needs, including underlying data models and scripts
- History of proactively identifying forward-looking data engineering strategies, utilizing cutting-edge technologies, and implementing at scale
- Understanding of statistical modeling, machine learning and data mining concepts
- Demonstrable critical thinking and analytical skills, including the ability and confidence to make conclusions and recommendations from data
- Experience interacting with key stakeholders in different fields, interpreting challenges and opportunities into actionable engineering strategies
- Bachelor’s degree in Computer Science, Math, Statistics, Economics, or other quantitative field or cumulative relevant experience
Why You’ll Like Working for DigitalOcean:
- We have amazing people. We can promise you will work with some of the smartest and most interesting people in the industry. We work hard but we always have fun doing it. We care deeply about each other and take our “no jerks” rule very seriously.
- We value development. We are a high-performance organization that is always challenging ourselves to continuously grow. That means we maintain a growth mindset in everything we do and invest deeply in employee development. You’ll need to be great to get hired here and we promise you’ll get even better.
- We care about you. We offer competitive health, dental, and vision benefits for employees and their dependents, a monthly gym reimbursement to support your physical health, and a monthly commute allowance to make your trips to and from work easier.
- We invest in your future. We offer competitive compensation and a 401k plan with up to a 4% employer match. We also provide all employees with Kindles and reimbursement for relevant conferences, training, and education.
- We want you to love where you work. We have great office spaces located in the heart of SoHo NYC and Cambridge, and offer daily catered lunches to keep your hunger at bay. We’re also very remote-friendly—we use Slack to communicate across the company—and all remote employees have the opportunity to take an all-expense-paid trip to our office to get quality in-person time with the team at least once a year. We also allow employees to customize their workstations to meet their needs—whether remote or in office.
- We value diversity and inclusivity. We are an equal opportunity employer and we do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.