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Salary
≈ $15k – $35k per year (Estimated)
Location
In office (Chennai, Bengaluru)
Seniority
Middle · 3+ years exp

First seen by Alion on Sep 11, 2026.

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Welcome to Spectrum Consulting. Businesses require relevant talent to execute their customer commitments. Prospective Job seeker's need guidance and support to get the right jobs. We reinvent ourselves, constantly, to be able to serve the needs of our stakeholders. We Care.

Data Engineer (AWS & Databricks)

Experience : 3 - 5 Years

Role Summary :

We are looking for a Data Engineer with 3 - 5 years of experience specializing in Databricks and AWS to design, build, and optimize scalable data pipelines for analytical and BI workloads.

The ideal candidate brings hands-on expertise in PySpark, Delta Lake, and cloud-native integration between AWS and Databricks.

Key Responsibilities :

- Develop and maintain batch and streaming ETL/ELT data pipelines using Databricks and PySpark on AWS.

- Implement Lakehouse architectures using Delta Lake on Amazon S3 following the Medallion architecture (Bronze/Silver/Gold).

- Orchestrate data workflows using Databricks Workflows, AWS Step Functions, or Apache Airflow (MWAA).

- Write, tune, and optimize complex SQL queries for Amazon Redshift, Athena, and Databricks SQL.

- Configure secure data ingestion, storage, and cataloging leveraging AWS IAM, S3, and Databricks Unity Catalog.

- Build automated data validation checks and unit tests to ensure high data quality and pipeline reliability.

- Implement CI/CD deployment workflows using Git, Databricks Asset Bundles (DABs), or AWS CI/CD tools.

- Collaborate with data analysts, data scientists, and downstream stakeholders to deliver performant data models.

Technical Skills Required :

- Big Data & Compute: Databricks, Apache Spark, PySpark, Delta Lake

- AWS Services: Amazon S3, Redshift, Athena, IAM, CloudWatch

- Programming & Querying: Python, PySpark, Advanced SQL

- Orchestration: Databricks Workflows, Apache Airflow, or AWS Step Functions

- Streaming & Messaging: Apache Kafka, AWS Kinesis, or Spark Structured Streaming

- Data Governance & Formats: Unity Catalog, Parquet, Delta format

- Version Control & DevOps: Git, GitHub/GitLab, Docker basics

Good to Have :

- Databricks Certified Data Engineer Associate/Professional certification.

- Familiarity with AWS Glue, EMR, or DynamoDB.

- Experience with dbt (data build tool) for SQL transformations.

- Exposure to Generative AI integration, vector databases, or AWS Bedrock.

Skills

Databricks, PySpark, Delta Lake, AWS, Python, SQL, Apache Airflow, Data Build Tool, Data Engineering, Data Management

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