Junior Data Engineer

Saaf Finance

IndiaremotePosted 1 month ago

Skill Required

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Key highlights

Role designed for freshers or engineers with up to 2 years of experience.
Requires a Bachelor’s degree in Computer Science, Computer Engineering, IT, or a related field.
Must have basic SQL and Python proficiency.
Involves working with cloud data warehouses such as Snowflake or Databricks.
Provides mentorship and hands‑on exposure to modern data engineering tools.
No salary or compensation details disclosed.

Role overview

SAAF Finance is a technology‑driven lending platform building the data and automation backbone for modern mortgage and loan origination. The Data Engineering team designs and maintains the pipelines and platforms that power analytics, underwriting insights, reporting, and AI‑driven decisioning. This entry‑level Data Engineer role is aimed at early‑career engineers (freshers or up to 2 years of experience) who want strong mentorship and hands‑on exposure to modern data engineering tools and practices.

Responsibilities

Assist in building and maintaining data pipelines under the guidance of senior engineers.
Write, test, and optimize SQL queries to extract, transform, and validate data.
Support batch data ingestion and basic ETL/ELT workflows using Python.
Help maintain data quality by running basic checks and flagging inconsistencies.
Learn and work within our cloud data warehouse (Snowflake/Databricks) under supervision.
Document your work — data flow notes, basic technical specs, and test cases.
Participate in code reviews and incorporate feedback to improve code quality.
Collaborate with analytics and business teams to understand data needs.
Take a proactive approach to learning new tools, languages, and best practices.
Communicate progress and blockers clearly with your team.

Requirements

Bachelor's degree in Computer Science, Computer Engineering, IT, or a related field.
Basic understanding of SQL — able to write simple SELECT, JOIN, and aggregation queries.
Basic knowledge of Python programming.
Basic awareness of data warehousing concepts (tables, schemas, ETL at a conceptual level).
Understanding of relational databases and data structures.
Strong willingness to learn, good analytical thinking, and attention to detail.
Good verbal and written communication skills.
1–2 years of hands‑on experience in a data engineering, analytics engineering, or similar role.
Working knowledge of SQL, including joins, aggregations, and basic performance tuning.
Hands‑on experience with Python.
Exposure to a cloud data warehouse or lakehouse platform (Snowflake, Databricks, Redshift, or BigQuery).
Familiarity with version control (Git/GitHub/Bitbucket).
Basic understanding of any cloud platform (AWS, Azure, or GCP).
Good problem‑solving skills and ability to work in an agile team environment.

Nice to have

Exposure to PySpark.
Basic exposure to ETL/data pipeline tools (e.g., Airflow, dbt, Fivetran).
Academic or internship project experience involving data pipelines or analytics.
Exposure to workflow orchestration tools such as Airflow.
Basic familiarity with streaming concepts (Kafka) or NoSQL databases.
Exposure to BI/visualization tools (Power BI, Tableau, Looker).
Basic knowledge of Docker or containerization concepts.