Data Engineer – Analytics & AWS

Health Catalyst; Hyderabad; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

Health Catalyst in Hyderabad, India, is seeking a Data Engineer – Analytics & AWS with 2–4 years of experience to contribute across data engineering and analytics. You will design and run scalable ETL/ELT pipelines, leverage AWS Glue, S3, Snowflake, Python, and SQL, and partner with analytics teams to deliver business-ready datasets.

This role also focuses on data quality, governance, and modern data warehousing practices within an Agile environment, with collaboration across cross-functional

Qualifications

• 2–4 years of experience in Data Engineering, Analytics Engineering, or related field.
• Strong hands-on SQL and Python.
• Experience building or supporting ETL/ELT pipelines.
• Hands-on with AWS Glue and S3.
• Working with Snowflake.
• Experience working with data for both engineering and analytics.
• Ability to learn new technologies in a fast-paced Agile environment.

Responsibilities

• Design, develop, and maintain ETL/ELT pipelines for data ingestion, transformation, and loading.
• Build data pipelines using AWS Glue and Amazon S3.
• Develop Python-based data processing and automation solutions.
• Build and optimize data warehouse solutions using Snowflake.
• Develop and support workflow orchestration using Airflow and related services.
• Work with event-driven and streaming workflows using Kafka.
• Troubleshoot production issues and improve pipeline performance, scalability, and reliability.
• Collaborate with engineers, analysts, and other stakeholders to understand requirements and deliver solutions.
• Document data processes, architecture, workflows, and operational procedures.
• Manage multiple projects and priorities based on business and team requirements.

Skills

AWS

Data & Analytics

SQL

Snowflake

Python

ETL/ELT

Data Warehousing

Data Quality

Data Analysis

Airflow

Kafka

Education

Bachelor's degree in Computer Science, Information Technology, Engineering

Tools

AWS Glue

AWS S3

AWS Lambda

AWS SNS

Snowflake

Airflow

Kafka

Pentaho

EMR

Redshift

Athena

Step Functions

IAM

Job description

We are looking for a Data Engineer – Analytics & AWS with 2–4 years of experience who enjoys working across both data engineering and data analytics.

In this role, you will build and support scalable data pipelines while also working with data for analysis, data quality, transformation, and analytics use cases. You will work with modern AWS and Snowflake technologies and collaborate with cross-functional teams to deliver reliable, business-ready data solutions.

The ideal candidate has strong hands‑on experience with AWS Glue, S3, SQL, Python, and Snowflake, along with a good understanding of ETL/ELT and data analytics.

What You'll Work On:

• Design, develop, and maintain ETL/ELT pipelines for data ingestion, transformation, and loading.
• Build and support data pipelines using AWS Glue and Amazon S3.
• Develop Python-based data processing and automation solutions.
• Build and optimize data warehouse solutions using Snowflake.
• Develop and support workflow orchestration using Airflow and related services.
• Work with event-driven and streaming workflows using Kafka.
• Troubleshoot production issues and improve pipeline performance, scalability, and reliability.

Data Analytics

• Analyze data to identify trends, issues, and insights.
• Use SQL and Snowflake for data analysis, validation, and investigation.
• Support data quality and reconciliation activities across multiple data sources.
• Work with business and technical teams to understand analytics requirements.
• Translate data findings into practical technical solutions.
• Support analytics and reporting use cases across multiple projects.

Key Responsibilities:

• Work on projects across Data Ingestion, Data Transformation, Data Quality, and Data Analytics.
• Develop efficient SQL queries for transformation, validation, and analytics.
• Implement and maintain Snowflake capabilities including RBAC, ABAC, Snowpipes, and Dynamic Tables.
• Develop AWS Glue ETL workflows and Python Shell jobs.
• Work with AWS services including Glue, Lambda, SNS, and S3.
• Ensure data accuracy, consistency, security, and operational reliability.
• Investigate data issues and perform root‑cause analysis.
• Collaborate with engineers, analysts, and other stakeholders to understand requirements and deliver solutions.
• Document data processes, architecture, workflows, and operational procedures.
• Manage multiple projects and priorities based on business and team requirements.

Core Technical Skills:

• AWS
• AWS Glue
• AWS S3
• AWS Lambda
• AWS SNS
• Data & Analytics
• SQL
• Snowflake
• ETL/ELT
• Data Warehousing
• Data Quality
• Data Analysis
• Programming
• Python
• Data & Workflow Technologies
• Airflow
• Kafka

What You'll Bring:

• 2–4 years of experience in Data Engineering, Analytics Engineering, Software Development, or a related field.
• Strong hands‑on experience with SQL and Python.
• Experience building or supporting ETL/ELT pipelines.
• Hands‑on experience with AWS Glue and S3.
• Working experience with Snowflake.
• Experience working with data for both engineering and analytical purposes.
• Strong analytical, debugging, and problem‑solving skills.
• Ability to work on multiple projects and manage changing priorities.
• Strong collaboration and communication skills.
• An ownership mindset and attention to detail.
• Ability to learn new technologies and work effectively in a fast‑paced Agile environment.

Good to Have:

• Experience with Pentaho for ETL/data integration.
• Exposure to AWS EMR, Redshift, Athena, Step Functions, IAM, CloudWatch, or EventBridge.
• Working knowledge of Kafka and streaming architectures.
• Familiarity with data lakes and distributed systems.
• Understanding of data governance, security, and access controls.
• Experience working with analytics, reporting, or business intelligence use cases.
• Relevant certifications such as:
• SnowPro Advanced: Data Engineer

Education:

Bachelor's degree in Computer Science, Information Technology, Engineering, or a related field.

Why This Role?

Join a collaborative Data Engineering team working on diverse projects across data ingestion, data transformation, data quality, and analytics. This role offers hands‑on exposure to both Data Engineering and Analytics, with opportunities to work with modern AWS, Snowflake, Python, and SQL technologies.