Senior Data Engineer

Orita

5d•Remote

About The Position

Orita builds AI customer segments for many of the best brands in the world. Orita’s algorithms help brands understand who wants to hear from them, when, and through what channel. By messaging prospects and customers when they’re actually listening, you’re able to make a bunch of money. In a world where acquisition costs are skyrocketing, fixing retention and driving LTV is the key to profitable growth. We need a talented Data Engineer to help us handle massive amounts of data efficiently and reliably. You'll play a crucial role in unifying our data pipeline with an event taxonomy and normalization layer, ensuring our machine learning models have high-quality data to work with. You’ll have a huge impact on our product, our culture, and building something great from the ground up. You will be on a team of engineers, reporting to our Director of Data. We look like a SaaS company, but we’re really building the data science and machine learning company to support the world’s best brands. You will work closely with our talented teams to enhance our data infrastructure and drive innovation in our data processing capabilities.

Requirements

Proven experience as a Data Engineer, with a strong track record of delivering successful projects in data-intensive environments.
5+ years of experience in data engineering.
Expertise in Python and SQL for data processing and manipulation.
Hands-on experience with big data tools.
Comfort with async Python.
Proficiency with DAG tools such as Airflow, dbt, or Dagster.
Willingness to work on reporting one day, and deep infrastructure the next.
Experience in building and optimizing data pipelines, architectures, and data sets.
Strong understanding of data modeling, data warehousing, and ETL/ELT development.
Familiarity with cloud platforms like AWS, GCP, or Azure, and experience deploying data solutions in the cloud.
Familiarity with dev-ops best practices, container technologies and continuous integration.
Knowledge of best practices in data governance, data quality, and data security.
Strong analytical and problem-solving skills with keen attention to detail.
Excellent communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.
Strong sense of responsibility and ownership; you own projects end-to-end with a bias for solving problems and shipping impactful features into production that are well-tested.
Intellectual curiosity. You enjoy iterating and improving systems, always seeking better ways to solve complex problems.

Nice To Haves

Background in data science or machine learning.
Experience with A/B testing frameworks in big data environments.
Knowledge of MLOps practices and tools.
Experience working in the ecommerce or marketing technology space.
Infrastructure experience, particularly with Google Cloud Platform.
Building reliable integration platforms with third-party APIs and services.

Responsibilities

Design and build scalable and reliable data pipelines to handle large volumes of data from various sources.
Unify our data pipeline by developing an event taxonomy and normalization layer for consistent and accurate data.
Develop and maintain workflows using Airflow, dbt, and Spark.
Collaborate with data scientists and machine learning engineers to facilitate seamless data integration for model training and deployment.
Optimize data retrieval and develop data models for storage and analysis.
Ensure data quality, integrity, and security throughout the data lifecycle.
Implement ETL/ELT processes and data integration solutions.
Contribute to feature engineering efforts to enhance model performance.
Set up and analyze A/B testing in big data environments.
Monitor and troubleshoot data pipelines and workflows to maintain optimal performance.