Data Engineer

Weekday (YC W21)

. Design and implement robust ETL pipelines that ingest, transform, and store data from diverse sources into a unified data warehouse .

Posted 11/10/2026full-timeRemote • IndiaMid-LevelSenior💰 ₹2,000,000 - ₹3,000,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and implementing ETL pipelines, optimizing SQL queries, and integrating analytics tools like Posthog and Streamlit. Proficient in Python programming and data governance practices to ensure compliance and performance in data engineering.

Highest-signal resume keywords

ETL Pipeline DevelopmentPython ProgrammingSQL OptimizationPosthog IntegrationStreamlit Dashboard Creation

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

ETL DevelopmentSQLPythonData Pipeline ArchitectureModel EvaluationData GovernanceData VisualizationLangChain WorkflowsDatabase ManagementData Analysis

Soft Skills

Problem-SolvingCollaborationDocumentationAgile MethodologyContinuous Learning

Tools & Technologies

PostgreSQLBigQuerySnowflakeStreamlitDeepevalLangChain

Industry Keywords

Data EngineeringData WarehousingData GovernanceAnalytics PlatformsBehavioral Data Analysis

Tech Stack

Tools & technologies

BigQueryETLPostgresPythonSnowflakeSQL

About the role

Key responsibilities & impact

Design and implement robust ETL pipelines that ingest, transform, and store data from diverse sources into a unified data warehouse
Build and maintain dashboards and data visualizations using Streamlit to provide real-time insights for product, marketing, and engineering teams
Integrate Posthog event data into the analytics pipeline and create segmented user funnels for product optimization
Develop and maintain LangChain workflows to power conversational AI features that interact with structured data
Employ deepeval to automate evaluation of machine learning models, ensuring model performance aligns with business KPIs
Optimize SQL queries and database schemas for speed, reliability, and scalability across large datasets
Collaborate with data scientists to provision and manage queryable data lakes and data marts
Implement data governance, security, and privacy controls to comply with industry regulations
Monitor pipeline health, troubleshoot failures, and iterate on performance enhancements
Document data architecture, pipeline logic, and best practices for internal and external stakeholders

Requirements

What you’ll need

3+ years of professional experience building data pipelines and analytics platforms in a production environment
Strong programming skills in Python, with extensive experience in writing reusable, testable code
Deep knowledge of SQL and experience with modern database systems such as PostgreSQL, BigQuery, or Snowflake
Proven track record of integrating Posthog for behavioral data ingestion and analysis
Hands-on experience with deepeval or similar model evaluation frameworks
Familiarity with LangChain for building LLM-driven data workflows and prompt engineering
Solid understanding of Streamlit or comparable open-source toolkits for building interactive dashboards
Ability to collaborate effectively with cross-functional teams in a remote, agile environment
Strong problem-solving mindset, with the capacity to troubleshoot complex data issues under tight deadlines
Commitment to code quality, documentation, and continuous learning in the fast-moving data engineering landscape