We are seeking a skilled Data Engineer who will architect, build, and maintain the data infrastructure that powers business intelligence, analytics, and machine learning initiatives. If you are passionate about designing scalable data pipelines, optimizing data architectures, and enabling data-driven decision-making across organizations, this role offers an exciting opportunity to shape the future of our data ecosystem.
As a Data Engineer at our organization, you will design and implement robust ETL/ELT pipelines, build data warehouses and lakes, ensure data quality and governance, and collaborate with data scientists, analysts, and business stakeholders to deliver reliable, accessible data solutions. You’ll work with cutting-edge big data technologies and cloud platforms to process petabytes of data and enable real-time analytics.
We offer competitive compensation ranging from $95,000 to $165,000 based on experience, comprehensive benefits including health, dental, vision, and 401(k) matching, flexible remote work options, continuous learning opportunities through certifications and training, and access to state-of-the-art data platforms and tools. Our data-driven culture values innovation, quality, and the transformative power of well-engineered data solutions.
Objectives of this Role
- Design and build scalable, reliable data pipelines that ingest, process, and deliver data across the organization
- Develop and maintain data warehouse and data lake architectures that support analytical and operational needs
- Ensure data quality, consistency, and accessibility through robust validation and monitoring systems
- Collaborate with stakeholders to understand data requirements and deliver solutions that enable insights
- Optimize data systems for performance, cost-efficiency, and scalability
- Implement data governance, security, and compliance best practices
- Enable self-service analytics through well-documented, accessible data products
Your Tasks
- Build and maintain ETL/ELT pipelines using Apache Spark, Airflow, or cloud-native tools
- Design dimensional data models and implement data warehouse solutions using Snowflake, BigQuery, or Redshift
- Develop real-time streaming data pipelines using Kafka, Kinesis, or Pub/Sub
- Implement data quality frameworks including validation, monitoring, and alerting systems
- Create and maintain data catalogs and metadata management systems
- Build APIs and services for data access and integration
- Optimize SQL queries and database performance for large-scale data processing
- Implement data partitioning, indexing, and compression strategies
- Manage data lifecycle including archival, retention, and deletion policies
- Collaborate with data scientists to productionize machine learning models
- Work with analysts to design efficient reporting and visualization solutions
- Implement data security measures including encryption, access controls, and audit logging
- Document data pipelines, schemas, and business logic for knowledge sharing
- Monitor data pipeline performance and troubleshoot issues
- Participate in on-call rotations for critical data systems
Required Skills and Qualifications
- Bachelor’s degree in Computer Science, Engineering, Mathematics, or related field
- 3+ years of experience in data engineering or related roles
- Strong proficiency in SQL and at least one programming language (Python, Scala, Java)
- Experience with big data technologies (Spark, Hadoop, Hive, Presto)
- Hands-on experience with cloud data platforms (AWS, GCP, Azure)
- Knowledge of data modeling concepts and dimensional modeling
- Experience with workflow orchestration tools (Airflow, Luigi, Dagster)
- Understanding of distributed systems and data processing at scale
- Familiarity with version control systems and CI/CD practices
- Strong problem-solving and analytical thinking skills
- Excellent communication skills for technical and non-technical audiences
Preferred Skills and Qualifications
- Master’s degree in a quantitative field
- Experience with streaming technologies (Kafka, Flink, Storm)
- Knowledge of NoSQL databases (MongoDB, Cassandra, DynamoDB)
- Familiarity with containerization and orchestration (Docker, Kubernetes)
- Experience with data visualization tools (Tableau, PowerBI, Looker)
- Understanding of machine learning pipelines and MLOps
- Knowledge of graph databases and processing (Neo4j, GraphX)
- Experience with data governance tools and practices
- Certifications in cloud data platforms or big data technologies
- Contributions to open-source data projects
- Experience with data mesh or data fabric architectures
Technology Stack and Tools
Work with cutting-edge data technologies and platforms in a modern data stack. You’ll have access to cloud-native data services, distributed computing frameworks, advanced analytics tools, and the latest innovations in data processing. Our infrastructure handles petabytes of data across batch and streaming pipelines, supporting everything from traditional BI to advanced machine learning applications.
Career Growth and Development
As a Data Engineer, you’ll have clear paths for career advancement including senior engineer, lead engineer, or data architect positions. We support professional growth through certifications (AWS Data Analytics, GCP Data Engineer), conference attendance (Strata, DataEngConf), online learning platforms, and hands-on experience with emerging technologies. Many of our engineers have advanced to principal engineer roles, data platform leadership, or specialized in areas like streaming or ML engineering.
Impact and Innovation
Your work will directly enable data-driven decision-making across the organization, from executive dashboards to customer-facing features. You’ll build the foundation that powers analytics, machine learning, and business intelligence initiatives that drive growth and innovation. Your contributions will transform raw data into valuable insights that shape product development, customer experience, and business strategy.
