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Data Analytics Engineer

Data Analytics Engineer

Monocept

0 years

0 Lacs

gurugram haryana india

Posted:15 hours ago| Platform:

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Skills Required

data analytics pyspark sql design engineering etl spark macros governance testing resolve documentation transparency collaboration engagement reporting architecture devops git code joins query aws azure databricks orchestration airflow kafka

Work Mode

On-site

Job Type

Full Time

Job Description

We are looking for a Data Engineer with strong hands-on experience in PySpark, dbt, and SQL to design, build, and optimize scalable data pipelines and analytics-ready data models. The ideal candidate will work closely with analytics, BI, and data science teams to ensure reliable, high-quality, and well-governed data across the organization.

Key Responsibilities

Data Engineering & Pipelines

Design, develop, and maintain scalable ETL/ELT pipelines using PySpark.Process large-scale structured and semi-structured datasets efficiently.Optimize Spark jobs for performance, cost, and reliability.Handle batch and (if applicable) near-real-time data processing.

Data Modelling & Transformation

Build and maintain dbt models following best practices (staging, intermediate, marts).Implement dimensional models (fact and dimension tables) for analytics use cases.Write efficient, maintainable, and optimized SQL queries.Apply data transformations using dbt macros, tests, and documentation.

Data Quality & Governance

Implement data validation and testing using dbt tests.Monitor data pipelines and resolve data quality issues proactively.Maintain data lineage and documentation to ensure transparency and trust.

Collaboration & Stakeholder Engagement

Work closely with data analysts, BI developers, and business stakeholders to understand data requirements.Support analytics and reporting use cases by delivering clean, reliable datasets.Participate in design reviews and contribute to data architecture decisions.

DevOps & Best Practices

Use version control (Git) for code management.Follow CI/CD best practices for data pipelines and dbt deployments.Ensure secure and compliant data handling.Write clean, modular, and well-documented code.

Required Skills & Qualifications

Must-Have

Strong experience with PySpark for large-scale data processing.Hands-on experience with dbt (models, tests, documentation, macros).

Advanced SQL Skills (complex Joins, Window Functions, Query Optimization).

Solid understanding of data warehousing concepts.Experience working with large datasets in production environments.

Good to Have

Experience with cloud data platforms (AWS / Azure / GCP).Familiarity with Spark on Databricks, EMR, or equivalent platforms.Knowledge of orchestration tools (Airflow, Dagster, Prefect).Exposure to streaming technologies (Kafka, Spark Structured Streaming).Understanding of data lake / lakehouse architectures.

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Data Analytics Engineer