Senior Data Engineer

Byrider•Carmel, IN

1h•Hybrid

About The Position

We seek a highly skilled and experienced Senior Data Engineer to join our dynamic team. As a Senior Data Engineer, you will play a critical role in designing, developing, and maintaining our data infrastructure, ensuring the availability, reliability, and performance of our data systems. You will drive the data engineering initiatives and collaborate closely with cross-functional teams to support data-driven decision-making within the organization. You must also be skilled at finding solutions to problems while keeping the environment well-structured, stable, and secure. This position reports to the Director of Solutions Engineering.

Requirements

Proven experience as a Senior Data Engineer, with a strong understanding of information data security and data access controls
Proficiency in programming languages such as Python, Java, or Scala
Strong SQL skills and deep experience with database technologies (e.g., SQL, NoSQL)
Expertise in data warehousing and ETL tools (e.g., Kodda, AWS Glue)
Extensive knowledge of data modeling techniques and data warehouse design
Excellent problem-solving and communication skills
Ability to work collaboratively in a team, adapt to a fast-paced, evolving environment, and work individually to accomplish goals
Experience with cloud platforms (e.g., AWS, Azure) and containerization (e.g., Docker, Kubernetes)
Strong commitment to data accuracy, data quality, and data security
Ability to work with minimal supervision.
3+ years of Python experience
3+ years of SQL experience
3+ years of experience with data modeling and data warehousing
Experience with frameworks and products described in the “Products and Stacks” section
Experience with standard tools such as Atlassian product suite, AWS and Databricks

Nice To Haves

Technology-related certifications are a plus

Responsibilities

Data Architecture: Lead the design and development of complex data pipelines, data models, and data integration solutions that meet business requirements and performance standards
Data Ingestion: Architect and oversee the development of efficient and scalable data ingestion processes from various sources, including databases, APIs, and external data providers (AWS Redshift, AWS DynamoDB, AWS S3, MS SQL, etc.)
Data Transformation: Develop advanced data transformation pipelines and ETL processes to convert raw data into structured and meaningful formats
Data Quality: Establish and enforce best practices for data quality, validation, cleaning, and error handling
Data Storage: Manage and optimize data storage solutions, including databases, data lakes, and cloud storage services
Data Security: Implement advanced data security measures and access controls to protect sensitive data
Performance Optimization: Continuously monitor and optimize the performance of data pipelines and databases, implementing best practices to ensure efficient data processing
Collaboration: Collaborate closely with leadership, analysts, and business stakeholders to understand their data requirements and deliver advanced data solutions that meet their evolving needs
Innovation: Stay at the forefront of emerging data engineering technologies, industry trends, and best practices, and drive innovation within the data engineering domain
Documentation: Maintain comprehensive documentation of data pipelines, data models, and processes, and ensure knowledge sharing within the team