Pyspark Developer

Sightspectrum; Chennai District; On-site; INR 900,000 - 1,300,000; Full time

Job summary

The Senior PySpark ETL Engineer at Sightspectrum in Chennai designs, builds, and operates scalable PySpark data pipelines to handle high-volume batch data and, optionally, streaming data. You will own end-to-end production pipelines, ensuring performance, data quality, and cost efficiency across enterprise data platforms.

Strong Python, Spark, SQL, and data modeling skills are required, along with experience in ETL/ELT patterns, incremental loads, watermarking, and robust testing and deployment

Responsibilities

• Pipeline Development & Ownership: Design, implement, and maintain high-performance PySpark ETL pipelines.
• Own pipelines end-to-end, including development, deployment, monitoring, and production support.
• Ensure pipelines are scalable, fault tolerant, and runnable.

Job description

Role & responsibilities : Pyspark ETL engineer

Position: Senior PySpark ETL Engineer

Role Summary

The Senior PySpark ETL Engineer is responsible for designing, building, optimizing, and operating scalable data pipelines using Apache Spark (PySpark). This role focuses on highvolume batch (and optionally streaming) data processing, ensuring performance, reliability, data quality, and cost efficiency across enterprise data platforms.

The position requires strong python, hands on Spark expertise, deep SQL and data modeling knowledge, and the ability to own pipelines end toend in production.

Mandatory Requirements

1012 years of overall IT experience, with strong focus on data engineering and ETL.

3+ years of hands on experience with PySpark / Apache Spark in production environments.

Strong experience designing and implementing ETL / ELT pipelines at scale.

Excellent knowledge of SQL and relational data concepts.

Experience handling large datasets in distributed environments.

Strong ownership mindset, problemsolving skills, and ability to independently handle production pipelines.

Core Technical Skills

PySpark & Spark Engineering

Deep expertise in PySpark:

DataFrames, Spark SQL, window functions, joins, aggregations

Spark execution model (DAGs, stages, tasks)

Strong hands on experience with:

Partitioning strategies

Shuffle optimization

Broadcast vs sortmerge joins

Caching / persisting

Handling data skew and memory spills

Proven ability to debug and optimize slow Spark jobs.

ETL & Data Engineering

Strong knowledge of ETL/ELT design patterns:

Incremental loads

Watermarking

Idempotent pipeline design

Reprocessing and backfill strategies

Experience implementing:

SCD Type 1 / Type 2

Deduplication and late arriving data handling

Ability to design reusable transformation frameworks and common utilities.

Experience building source to target reconciliation and data quality checks.

Data Storage & SQL

Excellent SQL skills including:

Complex joins

Subqueries and CTEs

Window functions

Query optimization

Experience working with:

RDBMS sources (Postgres, MySQL)

Data lake storage using Parquet / ORC

Experience with partitioned datasets and compaction strategies.

Cloud & Big Data Platforms

Hands on experience with at least one Spark platform:

AWS EMR

Spark on Kubernetes

Experience working with cloud storage:

S3

Familiarity with orchestration tools:

Airflow, Databricks Workflows, ADF, or equivalent.

Responsibilities

Pipeline Development & Ownership

Design, implement, and maintain highperformance PySpark ETL pipelines.

Own pipelines end toend, including development, deployment, monitoring, and production support.

Ensure pipelines are scalable, fault tolerant, and re runnable.

Implement incremental processing and efficient data movement strategies.

Performance & Reliability

Identify and fix Spark performance bottlenecks.

Optimize resource usage and reduce execution time and cost.

Handle production issues related to:

Job failures

Data corruption

SLA breaches

Perform root cause analysis and implement permanent fixes.

Data Quality & Governance

Implement strong data quality validations, checks, and reconciliation mechanisms.

Ensure correctness, completeness, and freshness of datasets.

Follow enterprise standards for:

Data retention

Auditability

Schema evolution

Engineering Excellence

Write clean, maintainable, and testable PySpark code.

Conduct code reviews and guide junior engineers.

Follow best practices for:

Version control (Git)

CI/CD

Logging and monitoring

Maintain clear documentation and operational runbooks