Key Responsibilities :

- Partner with product, business, and engineering teams to translate business requirements into scalable data solutions.

- Design, build, and maintain reliable ETL/ELT pipelines from multiple data sources.

- Develop high-quality, well-modeled datasets for reporting, dashboards, and advanced analytics.

- Ensure data accuracy, completeness, and consistency using automated validation and monitoring frameworks.

- Implement data governance, privacy, and security best practices for financial and user data.

- Build reusable data frameworks, libraries, and tooling to enable self-service analytics.

- Optimize performance of batch and streaming data pipelines.

- Troubleshoot and resolve data quality, latency, and pipeline failures.

Must Have :

- Bachelor's or Master's degree in Computer Science, Engineering, Statistics, or related field (or equivalent experience).

- 5+ years of experience building and managing data systems at scale.

- Strong programming skills in Python (or Scala/Java) and advanced SQL.

- Experience with modern data warehouses such as BigQuery, Snowflake, or Redshift.

- Experience with orchestration tools such as Airflow.

- Hands-on experience with Spark, Databricks, Kafka, or similar processing frameworks.

- Strong understanding of data modeling, schema design, and performance optimization.

- Experience working on cloud platforms such as AWS, GCP, or Azure.

Nice to Have :

- Experience in fintech, payments, lending, or financial analytics.

- Familiarity with BI and visualization tools like Looker, Tableau, or Superset.

- Experience with event tracking systems such as Segment, RudderStack, or Snowplow.

- Knowledge of experimentation, growth analytics, and A/B testing frameworks.