Data Engineer

Delightree; Bengaluru; Sur place; INR 1 500 000 - 2 300 000; Plein temps

Résumé du poste

Delightree is looking for a data engineer to design, build, and own data pipelines and integrations end-to-end. You will own new customer sources, maps, and the transforms behind them, with a path to senior data engineer as you grow.

You will work with Python, pandas/polars, and SQL to create robust data models and scalable ETL/ELT processes, deploying with AWS, Docker, and ClickHouse. A strong emphasis on testing, logging, and data quality is offered.

Qualifications

• 2–3 years of professional data engineering or backend work with substantial data work.
• Strong, clean, typed, testable code; comfortable with pandas/polars for transforms.
• Solid SQL and understanding of relational/columnar data modeling; able to write and reason about non-trivial queries.
• Experience building ETL/ELT pipelines and consuming REST APIs (auth, pagination, rate limits, retries).
• Familiarity with cloud data infra: AWS (S3 especially), containers/Docker, and a data warehouse/OLAP store (ClickHouse, BigQuery, Snowflake, Redshift, or similar).
• Version control (Git) and working within a PR/code-review flow.
• Nice-to-have: ClickHouse; dlt/Airbyte/Fivetran; Dagster/Airflow; dbt.
• Experience with multi-tenant data or ingestion from third-party/partner APIs.
• Exposure to data-quality tooling (Great Expectations, dbt tests) or testing frameworks (pytest).

Responsabilités

• Build new integrations end-to-end: source connectors (SaaS APIs via Nango, API-key sources via Python clients, and warehouse sources).
• Design transforms and data models for the sources you own; map data into the shared schema.
• Write clean, tested, idempotent pipeline code to engineering standards with tests and logging.
• Onboard new customers: bring up sources, map entities, handle data-quality quirks.
• Own operability: freshness checks, backfills, and fix pipelines when they break.

Connaissances

Python

Pandas/Polars

SQL

Data modeling

ETL/ELT pipelines

REST APIs

AWS

Docker

ClickHouse

Git

Outils

Dagster

Airflow

dbt

Great Expectations

Description du poste

You'll design, build, and own data pipelines and integrations end-to-end, new customer sources, new connectors, and the transforms and data models behind them. We're looking for someone who takes full ownership of what they build and is a strong prospect to grow into a senior data engineer in the near future.

Responsibilities

• Build new integrations end-to-end: source connectors (SaaS APIs via Nango, API-key sources via Python clients, and warehouse sources), the extract/transform/load code, and the data models that land clean data into our ClickHouse silver/gold tables. You own your pipelines from the first line to production.
• Design transforms and data models for the sources you own: deciding how a new customer's data maps into our shared schema, not just implementing someone else's spec.
• Write clean, tested, idempotent pipeline code to our engineering standards: unit tests, schema/data-quality validation, structured logging, and docstrings.
• Onboard new customers: bring up their sources, map their entities, and solve the data-quality and modelling quirks that make every customer different.
• Own the operability of what you build: freshness checks, backfills, and fixing your pipelines when they break (because the person who built it fixes it best).

  Requirements

  • 2-3 years of professional data engineering (or backend engineering with substantial data work).
  • Strong, clean, typed, testable code; comfortable with pandas/polars or equivalent for transforms.
  • Solid SQL and understanding of relational/columnar data modeling; able to write and reason about non-trivial queries.
  • Experience building ETL/ELT pipelines and consuming REST APIs (auth, pagination, rate limits, retries).
  • Familiarity with cloud data infra: AWS (S3 especially), containers/Docker, and a warehouse or OLAP store (ClickHouse, BigQuery, Snowflake, Redshift, or similar).
  • Version control (Git) and working within a PR/code-review flow.
  • Nice-to-have: ClickHouse specifically; dlt/Airbyte/Fivetran; Dagster/Airflow; dbt.
  • Experience with multi-tenant data or ingestion from third-party/partner APIs.
  • Exposure to data-quality tooling (Great Expectations, dbt tests) or testing frameworks (pytest).

  Traits and soft skills

  • Ownership: you finish what you pick up, including the unglamorous parts (tests, docs, monitoring).
  • Curiosity and willingness to learn: you ask good questions and ramp fast on an unfamiliar stack.
  • Attention to detail: you care that row counts reconcile and that bad data doesn't slip downstream.
  • Clear communicator: you can explain what broke and what you did, and you write things down.
  • Self-starter: you take a problem and drive it to a shipped, working pipeline without needing constant hand-holding.