Data Engineer

**Job Title : Data Engineer

Job Type: Full Time - Permanent

Location: San Francisco, CA (Onsite)

NOTE: Candidate must be from top Big Tech Companies (Meta, Google, Nvidia, Paypal, Aribnb, Coinbase, Stripe, Ramp, Palantir, Square, Dropbox, Uber, Scale AI, McKinsey \& Company)**

Job Description

This is not a traditional backend software engineering or pure data analytics position. The ideal candidate has directly built production-grade data pipelines and is highly proficient in SQL, Python, dbt, and modern ELT infrastructure.

Key Responsibilities

Build and maintain reliable pipelines that ingest data from MongoDB, Airtable, PostHog, production databases, SaaS platforms, and other sources.

Design dbt models that transform fragmented raw data into standardized, production-ready schemas.

Develop scalable and fault-tolerant ELT/ETL workflows using Fivetran, dbt, SQL, and Python.

Own data reliability across the full lifecycle from ingestion and transformation to downstream consumption.

Establish automated data-quality checks, monitoring, alerting, and documentation.

Diagnose pipeline failures, data discrepancies, schema changes, and performance bottlenecks.

Improve pipeline scalability, processing speed, cost efficiency, and maintainability.

Partner with Data Science, Engineering, Product, and Operations teams to understand requirements and deliver usable datasets.

Support analytics, reporting, experimentation, and machine-learning workflows.

Promote strong standards for data governance, lineage, schema design, and access management.

Required Qualifications

Professional experience in data engineering or software engineering with significant ownership of data infrastructure.

Advanced SQL skills, including complex transformations, joins, window functions, query optimization, and data validation.

Strong Python experience for pipeline development, automation, integrations, and data processing.

Hands-on experience building and maintaining production-grade ETL or ELT pipelines.

Practical experience with dbt and a cloud data warehouse such as Snowflake, BigQuery, Redshift, or Databricks.

Experience integrating data from multiple source types, including production databases, analytics platforms, APIs, and SaaS tools.

Strong understanding of dimensional modeling, schema design, transformation patterns, and data warehousing.

Experience implementing monitoring, testing, and data-quality controls.

Ability to investigate ambiguous data problems and take ownership through resolution.

Strong communication skills and comfort working across engineering and nontechnical teams.

Preferred Qualifications

Experience with Fivetran or a comparable managed ingestion platform.

Experience working with MongoDB, Airtable, PostHog, or similar tools.

Familiarity with workflow orchestration technologies such as Airflow, Dagster, or Prefect.

Experience supporting machine-learning pipelines, experimentation systems, or analytics platforms.

Familiarity with data lineage, governance, privacy, and access-control practices.

Experience working in a fast-paced startup or high-growth technology company.