Data and Analytics Engineering - Senior Associate/Manager

PwC Service Delivery Center

4 - 9 years

15 - 30 Lacs

kolkata hyderabad bengaluru

Posted:1 day ago| Platform:

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Skills Required

pyspark data engineering artificial intelligence spark sql python langchain node.js ci/cd llm machine learning deep learning sql pytorch mlops nosql langgraph azure data services react.js

Work Mode

Hybrid

Job Type

Full Time

Job Description

Job Summary

Years of Experience:

4-9 year(s)

Responsibilities

Design and build scalable batch and streaming pipelines using Python, PySpark, Spark SQL, and Databricks/Synapse.
Develop reusable ETL/ELT components and ingestion flows for common data formats (JSON, Parquet, CSV).
Integrate data from relational databases (PostgreSQL/SQL Server), NoSQL systems (MongoDB), APIs, and streaming platforms (Kafka/Event Hubs).
Implement and optimize data models, Delta Lake tables, partitioning, and ACID pipelines.
Troubleshoot production issues, improve performance, and ensure data quality and reliability.
Build pipeline monitoring, alerting, and CI/CD workflows using Azure DevOps or GitHub Actions.
Collaborate with Product Owners and Architects to convert requirements into technical plans.

Preferred Knowledge/Skills:

What You Must Have

Strong programming skills in
Python (mandatory)
, PySpark, Spark SQL, and SQL.
Proven experience with
Azure data services
(ADF, Synapse, ADLS, and Databricks).
Experience building high-volume batch and real-time pipelines using Spark and Kafka/Event Hubs.
Solid understanding of relational databases (PostgreSQL/SQL Server) and basic NoSQL concepts.
Familiarity with Git-based workflows, CI/CD, and containerization (Docker).
Strong fundamentals in debugging, optimization, and clean coding practices.
Ability to lead engineers, manage production environment SLAs, and deliver high-quality solutions.

What Sets You Apart

Experience with Delta Lake optimization and advanced Spark tuning.
Exposure to V
ector databases
or embeddings (e.g., PGVector) for AI/ML pipelines.
Familiarity with
LangChain/LangGraph
or similar GenAI frameworks.
Ability to design reusable ingestion frameworks and scalable data architecture patterns.
Strong focus on performance tuning, cost optimization, and platform reliability.
Excellent communication, stakeholder management, and decision-making skills.

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