Azure Data Engineer

Posted Yesterday
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Bangalore, Bengaluru Urban, Karnataka, IND
In-Office
Junior
Information Technology
The Role
Design, build, and automate end-to-end ETL/ELT pipelines and large-scale data processing using Azure and AWS services, Databricks/EMR, streaming tools, and data lakehouse technologies. Implement data modeling, transformation (dbt), quality checks, infrastructure as code, and CI/CD while ensuring observability and containerized deployments.
Summary Generated by Built In

What success looks like in this role:

End-to-End Pipeline Engineering: Build and automate robust ETL/ELT pipelines using Azure Data Factory (ADF), AWS Glue, and Apache Airflow.

· Distributed Computing: Develop large-scale data processing jobs using PySpark and Scala within Databricks or EMR environments.

· Streaming & Real-time Integration: Design and implement real-time data ingestion and processing layers using Apache Kafka, Confluent, or AWS Kinesis.

· Data Lakehouse : Manage and optimize cloud storage using ADLS Gen2 and S3, implementing ACID transactions with Delta Lake or Apache Iceberg.

· Advanced Data Modeling: Design highly performant schemas for cloud data warehouses like Snowflake, Amazon Redshift, or Google BigQuery.

· Data Transformation & Quality: Use dbt (data build tool) for modeling and implement automated quality checks using Great Expectations or Soda.

· Infrastructure & CI/CD: Deploy and manage data infrastructure using Terraform or CloudFormation, and maintain CI/CD pipelines via GitHub Actions or GitLab CI.

Technical Stack Requirements

· Cloud Platforms: Deep hands-on experience with Microsoft Azure (ADF, Synapse, Databricks) and AWS (S3, Glue, Athena, Lambda).

· Programming: Strong proficiency in Python (PySpark, FastAPI), SQL, and familiarity with Java or Scala.

· Big Data Tools: Experience with Apache Spark, Apache Flink, and Hadoop ecosystem.

· Databases: Strong knowledge of both Relational (PostgreSQL, MySQL) and NoSQL (MongoDB, Cassandra, or DynamoDB) databases.

· Containerization: Proficiency with Docker and Kubernetes (K8s) for deploying data services.

· Observability: Familiarity with monitoring tools like Prometheus, Grafana, or Datadog to track pipeline health.

#LI-SS1

You will be successful in this role if you have:

Experience: 2- 4years of professional experience in data engineering, backend engineering, or a related field.

· Education: Bachelor’s  Engineering, 

· Methodology: Strong understanding of Agile methodologies and the ability to work in a fast-paced, iterative environment.

· Soft Skills: Excellent problem-solving skills and the ability to explain complex technical concepts to non-technical stakeholders.

Preferred Certifications

· Azure Data Engineer Associate (DP-203).

· AWS Certified Data Engineer – Associate.

· Databricks Certified Professional Data Engineer.

Unisys is proud to be an equal opportunity employer that considers all qualified applicants without regard to age, blood type, caste, citizenship, color, disability, family medical history, family status, ethnicity, gender, gender expression, gender identity, genetic information, marital status, national origin, parental status, pregnancy, race, religion, sex, sexual orientation, transgender status, veteran status or any other category protected by law.

Local employment practices and rights may vary by jurisdiction and are subject to applicable local laws. This commitment includes our efforts to provide for all those who seek to express interest in employment the opportunity to participate without barriers.

 

If you are a US job seeker unable to review the job opportunities herein, or cannot otherwise complete your expression of interest, without additional assistance and would like to discuss a request for reasonable accommodation, please contact our Global Recruiting organization at [email protected]. US job seekers can find more information about Unisys’ EEO commitment here.

Skills Required

  • 2-4 years professional experience in data engineering or related field
  • Bachelor's degree in Engineering
  • Build and automate ETL/ELT pipelines using Azure Data Factory, AWS Glue, or Apache Airflow
  • Develop large-scale data processing jobs using PySpark and Scala in Databricks or EMR
  • Design and implement real-time ingestion using Apache Kafka, Confluent, or AWS Kinesis
  • Manage cloud storage with ADLS Gen2 and S3 and implement Delta Lake or Apache Iceberg
  • Design performant schemas for Snowflake, Amazon Redshift, or Google BigQuery
  • Use dbt for data modeling and implement automated data quality checks (Great Expectations or Soda)
  • Deploy and manage infrastructure using Terraform or CloudFormation
  • Maintain CI/CD pipelines using GitHub Actions or GitLab CI
  • Deep hands-on experience with Microsoft Azure (ADF, Synapse, Databricks) and AWS (S3, Glue, Athena, Lambda)
  • Strong proficiency in Python (including PySpark, FastAPI) and SQL
  • Familiarity with Java or Scala
  • Experience with Apache Spark, Apache Flink, and the Hadoop ecosystem
  • Knowledge of relational (PostgreSQL, MySQL) and NoSQL (MongoDB, Cassandra, DynamoDB) databases
  • Proficiency with Docker and Kubernetes for deploying data services
  • Familiarity with observability/monitoring tools such as Prometheus, Grafana, or Datadog
  • Strong understanding of Agile methodologies and ability to work iteratively
  • Excellent problem-solving and ability to explain complex technical concepts to non-technical stakeholders
  • Preferred: Azure Data Engineer Associate (DP-203), AWS Data Engineer Associate, Databricks Certified Professional Data Engineer

Unisys Compensation & Benefits Highlights

The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Unisys and has not been reviewed or approved by Unisys.

  • Fair & Transparent Compensation Fair & Transparent Compensation: Compensation terms at hire are often presented clearly and upfront, creating a straightforward “take it or leave it” expectation. Pay outcomes are also described as variable by role and geography, with some pockets viewed as satisfactory or above average.
  • Retirement Support Retirement Support: A 401(k) plan with an employer match is commonly described as part of the core package. The match is often characterized as a meaningful component of total rewards relative to other benefits.
  • Healthcare Strength Healthcare Strength: Core medical, dental, and vision coverage is described as available and broadly in line with a large IT-services employer. The underlying carrier network is sometimes viewed as solid even when cost concerns exist.

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The Company
Albany, NY
22,588 Employees
Year Founded: 1986

What We Do

Unisys is a global information technology company that builds high-performance, security-centric solutions for the most demanding businesses and governments on Earth. Unisys offerings include security software and services; digital transformation and workplace services; industry applications and services; and innovative software operating environments for high-intensity enterprise computing. We build better outcomes securely for our clients across the Government, Financial Services and Commercial

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