Role Summary

Data Engineers with hands-on expertise in PySpark and Fivetran to design, build, and maintain scalable data ingestion and transformation pipelines, at two levels: Junior (execution-focused, guided work) and Senior (ownership of architecture, performance, and mentoring). Both levels will work on integrating data from SaaS, database, and API sources into the cloud data warehouse, and building distributed processing jobs to support analytics and reporting.

Junior Data Engineer — Requirements

[Must-Have] 1–3 years of hands-on experience with PySpark (DataFrames, Spark SQL basics) — production or robust academic/project experience acceptable.
[Must-Have] Working knowledge of Fivetran or a similar ELT tool (Airbyte, Stitch) — setting up connectors, monitoring syncs.
[Must-Have] Solid SQL fundamentals and exposure to a cloud data warehouse (Snowflake / Redshift / BigQuery).
[Must-Have] Basic Python scripting ability for data tasks.
Exposure to at least one cloud platform (AWS / Azure / GCP), internship or project-level acceptable.
Eager to learn,



works well under guidance from senior engineers; not expected to design architecture independently.

Nice-to-have Skills (either Level)

Experience with Databricks and Delta Lake.
Familiarity with orchestration tools (Airflow, Databricks Workflows).
dbt experience for transformation/modeling.
Exposure to streaming technologies (Kafka, Spark Structured Streaming).
Relevant certifications (Databricks Certified Data Engineer, Fivetran certification, cloud data engineer certs).
Prior experience in [specific industry, e.g., healthcare, finance, retail], if relevant.

Responsibilities

Junior:

Build and maintain PySpark transformations under senior engineer guidance.
Set up and monitor assigned Fivetran connectors; escalate sync issues.
Write and troubleshoot SQL queries for data validation.
Document pipeline steps and support operational runbooks.

SCREENING / SOURCING NOTES FOR RECRUITER

For Senior: prioritize candid

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