Job Description & Details
This is a solid 6-month contract gig embedded within a major financial institution through Genesis10. You will be knee-deep in heavy data pipelines, making sure massive volumes of financial data move quickly and cleanly without breaking downstream systems.
What You'll Actually Be Doing
You'll spend your days designing, building, and maintaining robust Data Integration (ETL) pipelines that chew through massive datasets. Beyond writing pipeline code, you'll own the application lifecycle—keeping things well-managed, monitoring performance, and jumping in to triage and resolve production fires when things go sideways in the cloud environment.
The Core Tech Stack
You need serious muscle in Python, SQL, and Java, paired with workflow orchestrators like Airflow to keep everything moving synchronously. Because this is enterprise-scale finance, heavy-hitting big data frameworks like Spark, Hadoop, Hive, and MapReduce running on cloud data warehouses like Snowflake, Redshift, or managed EMR are absolute non-negotiables for this role.
Interview Expectations
Expect the interviewers to probe deeply into your architectural choices around handling data skew and job failures in distributed systems like Spark. They will likely ask you to debug a hypothetical production bottleneck in an Airflow DAG or a slow-running SQL query on Snowflake, testing your ability to troubleshoot under pressure and optimize cost and compute resources in the cloud.
Application Advice
Make sure your resume screams data engineering fundamentals by highlighting your hands-on experience with AWS or GCP alongside heavy ETL workloads. Explicitly mention keywords like Apache Spark, Airflow, Snowflake, and Java or Python to easily clear their automated ATS filters before a recruiter reviews your background.