Develop and maintain strong, trust-based relationships with key stakeholders to understand their business challenges and identify data-driven solutions.
Develop and maintain data infrastructure, pipelines, and solutions, leveraging data engineering expertise.
Design and deploy automated solutions for building, testing, monitoring, and deploying ETL data pipelines in a continuous integration environment.
Design, build, and launch advanced data models and visualizations to support multiple use cases across various products or domains.
Conceive, design, and implement Cloud Data Lakes, Data Warehouses, Data Marts, and Data APIs.
Complete the full lifecycle of ETL/ELT development, including design, mappings, data transformations, scheduling, and testing.
Apply knowledge of data engineering concepts, including data APIs, data availability, data quality, data management, metadata management, reference data management, data governance, data catalog, data virtualization, and data optimization.
Utilize data tools and platforms such as Starburst, Snowflake, Databricks, and programming languages like SQL, Python, and Shell Scripting.
Manage batch job operations using Autosys, perform data modeling (physical and logical), and utilize data catalog tools (e.g., Collibra).
Implement data governance, quality standards, and best practices to ensure data accuracy and security.
Troubleshoot and optimize data processing workflows, reduce latency and enhance the scalability of data systems.
Collaborate on Data Strategy and Solutions
Ensure compliance with data privacy and security regulations, employing best practices for data handling and storage.
Build and maintain data dictionaries and process documentation.

Requirements

8+ years of hands-on experience in data science with a focus on developing and implementing data-driven solutions in the Banking & Financial Services sector.
5+ years of experience with Data Lake Infrastructure, Data Warehousing, and Data Analytics tools.
3+ years of experience in Implementation of cloud platforms like Snowflake, Databricks, S3, Redshift.
3+ years of experience in SQL optimization and performance tuning, and development experience in programming languages like Python, PySpark, Scala.
Experience in implementing cloud-based data solutions using AWS services (EC2, S3, EKS, Lambda, API Gateway, Glue) and big data tools (Spark, EMR, Hadoop).
Hands-on experience in data profiling, data modeling, and data engineering using databases (Snowflake, RedShift, DB2, Oracle, SQL Server), ETL tools (Informatica IICS), and scripting languages (Python).
5+ years experience with job/workflow management tools (Autosys) and other pipeline orchestration tools
Experience with source control tools like Git and CI/CD practices.
Experience architecting end-to-end data pipelines with both cloud and on-premises stacks.
Experience building/operating highly available, distributed systems of data extraction, ingestion, and processing of large data sets.
Understanding of metadata management, data lineage, and data glossaries is a plus.
Working knowledge of agile development, including DevOps and DataOps concepts.
Understanding of serverless architectures (e.g., AWS Lambda).
Experience in Jira, Confluence, MS office.

Benefits

comprehensive health and wellness benefits
retirement plans
educational assistance and training programs
income replacement for qualified employees with disabilities
paid maternity and parental bonding leave
paid vacation, sick days, and holidays

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard skills

data engineeringETLdata modelingSQL optimizationdata profilingdata governancedata qualitydata transformationcloud data solutionsdata visualization

Soft skills

relationship buildingstakeholder managementcollaborationproblem solvingcommunication