You will be part of a team responsible for supporting new and existing customers in their data engineering needs.
You will guide customers to make the best technical decisions to achieve their goals
You will actively work across multiple customer accounts which you would need to track and report on their progress.
You will design, build, and operationalize complex data solutions, correct problems, apply transformations, and recommend data cleansing /quality solutions.
You will analyze sources to determine value and recommend data to include in analytical processes.
You will incorporate core data management competencies including data governance, data security and data quality.
You will collaborate within and across teams to support delivery and educate end users on data products / analytic environments.
You will perform data and system analysis, assessment and resolution for defects and incidents of moderate complexity and correct as appropriate.
You will test data movement, transformation code, and data components.
Requirements
5+ years of related experience in data engineering and data product development.
Experience in one or more of the following:
Data Engineering technologies (e.g., Spark, Hadoop, Kafka)
Databricks platform
data engineering and/or ML ops experience would be a huge bonus
Data Science and Machine Learning technologies (e.g., pandas, scikit-learn, HPO)
Data Warehousing (e.g., SQL, OLTP/OLAP/DSS)
Solid understanding of the end to end data analytics workflow
Proven problem solving skills including debugging skills
Strong verbal and written communication skills
Leadership
Intermediate leadership skills with a proven track record of self-motivation in identifying personal growth opportunities
Excellent time management and prioritization skills. Knowledge of public cloud platforms AWS, Azure, or GCP would be a plus
Domain Knowledge: Background working in the Financial Services industry, preferably in a banking environment.
Regulatory Compliance: Understand industry compliance requirements and standards, specifically in banking IT landscapes
Nice to have: Databricks Certification
Tech Stack
AWS
Azure
Cloud
Google Cloud Platform
Hadoop
Kafka
Pandas
Scikit-Learn
Spark
SQL
Benefits
Innovative Environment: Work with cutting-edge technologies and industry leaders in data engineering and AI.
Customer Impact: Make a real difference in how businesses leverage data for strategic decision-making.
Career Growth: Opportunities for professional development and career advancement.
Collaborative Culture: Join a supportive team that values collaboration and knowledge sharing.