Senior Data Engineers play a critical role on the Data and Analytics team, responsible for transforming data from disparate systems to provide insights and analytics for business stakeholders. The Data and Analytics team leverages cloud-based infrastructure to implement technology solutions that are scalable, resilient, and efficient. Collaborate with other Data Engineers, Data Analysts, Data Scientists, DBAs, cross-functional teams, and business partners.
The position offers the opportunity to help architect, design, implement and operate data engineering solutions - using Agile methodology - that empower users to make informed business decisions. The ideal candidate is self-motivated, self-directed, and has hands-on experience with all aspects of the software development lifecycle, from design to deployment.
Our team is searching for someone with a deep understanding of the full life data lifecycle and of the role that high-quality data plays across applications, machine learning, business analytics, and reporting. Strong candidates will exhibit solid critical thinking skills, the ability to synthesize complex problems, and a talent for transforming data to create solutions that add value to a myriad of business requirements.
Ideal candidates will have the demonstrated ability to facilitate and take ownership of assigned technical projects in a fast-paced environment. Excellent written and speaking communication skills are required as we work together in a collaborative cross-functional environment and interact with the full spectrum of business divisions.
- Bachelor of Science degree in Computer Science or equivalent.
- At least 7 years of post-degree professional experience, including:
- 4+ years development experience building and maintaining ETL pipelines
- 3+ years of Python development experience.
- Experience with AWS integrations such as Kinesis, Firehose, Aurora Unload, Redshift, Spectrum, Elastic Mapreduce, SageMaker and Lambda.
- Experience in mentoring junior team members through code reviews and recommending adherence to best practices.
- Deep understanding of writing test cases to ensure data quality, reliability and high level of confidence.
- Track record of advancing new technologies to improve data quality and reliability.
- Continuously improve quality, efficiency, and scalability of data pipelines.
- Expert skills working with SQL queries, including performance tuning, utilizing indexes, and materialized views to improve query performance.
- Advanced knowledge of both OLTP and OLAP environments with successful implementation of efficient design concepts. Proficiency with the design and execution of NoSQL database to optimize Big Data storage and retrieval.
- Experience with API code integrations with external vendors to push/pull data between organizations
- Familiarity with data orchestration pipeline using Argo or Airflow. Knowledge of analytic tools such as R, Tableau, Plotly, Python Pandas.
- Financial services industry experience is a plus