Data Analytics Lead Engineer

What you'll do

• Build, deploy, and manage end-to-end data pipelines for large-scale lending datasets across Mortgage and Personal Loans.
• Design and implement scalable data architectures on cloud platforms including data lakes and warehouses.
• Architect and implement various data schemas to meet specific performance and business requirements.
• Write and optimize complex SQL queries using distributed and parallel processing to improve pipeline efficiency.
• Monitor, diagnose, and resolve operational issues and data quality problems to ensure timely delivery.
• Apply generative AI tools to accelerate engineering tasks like code generation and query optimization.
• Translate business requirements into robust technical solutions in collaboration with analysts and governance teams.

What we're looking for

• 6+ years of experience building and managing data pipelines, warehouses, and lake solutions using Hadoop, Spark, Databricks, or similar technologies.
• Practical experience with cloud data platforms like Snowflake or Cloudera to build and automate ETL workflows.
• Fluency in Python, Scala, or Shell Scripting for data engineering and automation tasks.
• Ability to design and query relational and non-relational data stores using various schema models.
• Experience with workflow scheduling tools such as Autosys or Apache Airflow.
• Proficiency in DevOps practices including version control, build tools, unit testing, and monitoring.
• Experience with data visualization platforms like Tableau or Cognos.
• Bachelor's degree required; Master's degree preferred.
• Exposure to cloud AI/ML services (preferred).
• Familiarity with NoSQL databases such as HBase, MongoDB, or Cassandra (preferred).
• Databricks or cloud platform certification in AWS, Azure, or GCP (preferred).