Engineer

Role Overview

We are seeking a skilled Data Engineer to design build and maintain scalable data platforms and pipelines across AWS and Azure. The role will support analytics AI/ML and business intelligence use cases using modern data technologies including Databricks Snowflake SageMaker and Amazon Bedrock. The ideal candidate has strong cloud data engineering experience and collaborates closely with data scientists analytics teams and business stakeholders.

Key Responsibilities

Data Platform & Pipeline Development

Design develop and optimize scalable secure and reliable data pipelines using batch and streaming patterns.

Build and maintain ETL/ELT workflows ingesting data from structured and unstructured sources.

Implement data transformations and orchestration using Databricks (Spark) SQL and Python.

Develop and maintain data models optimized for analytics and reporting.

Cloud & Data Architecture

Implement data solutions on AWS and Azure leveraging native services.

Design and manage cloud storage solutions (e.g. S3 ADLS Gen2).

Build cloudagnostic or hybrid architectures supporting multicloud strategies when required.

Optimize performance scalability reliability and cost across platforms.

Analytics & Data Warehousing

Design and support Snowflake data warehouse solutions including schema design performance tuning and cost management.

Enable selfservice analytics and BI use cases through curated highquality datasets.

Partner with reporting and analytics teams to ensure data availability and accuracy.

AI / ML & Advanced Analytics Enablement

Support ML workflows by preparing and engineering features for SageMaker and Amazon Bedrock use cases.

Collaborate with data scientists to operationalize ML models (MLOps integration).

Enable GenAI and advanced analytics workloads through secure governed data access.

Data Quality Security & Governance

Implement data quality checks monitoring and validation frameworks.

Ensure compliance with security privacy and regulatory requirements.

Apply data governance standards including lineage metadata management and access controls.

DevOps & Automation

Implement CI/CD pipelines for data solutions using infrastructureascode (Terraform ARM CloudFormation).

Automate deployments monitoring logging and alerting.

Participate in oncall or support rotations as needed.

Collaboration & Stakeholder Engagement

Work closely with product owners business stakeholders architects and analytics teams.

Translate business requirements into scalable technical solutions.

Contribute to documentation standards and best practices across the data engineering team.

Required Skills & Experience

Technical Skills

• Strong experience with Python and SQL.
• Handson experience with Databricks (Apache Spark).
• Proven experience building solutions on AWS and/or Azure.
• Experience with Snowflake data warehouse.
• Experience supporting ML or AI workloads using SageMaker and Amazon Bedrock (or equivalent services).
• Knowledge of data integration tools APIs and message/streaming platforms.
• Understanding of data modeling principles and analytics use cases.
• Familiarity with DevOps concepts CI/CD and IaC.

Soft Skills

• Strong problemsolving and analytical skills.
• Ability to communicate technical concepts to nontechnical audiences.
• Collaborative mindset with experience working in agile teams.
• Attention to detail with a focus on data accuracy and reliability.

Education Requirements

Bachelors degree in Computer Science Engineering Information Systems Data Science or a related field.

Masters degree is a plus but not required.

Experience Requirements

5 years of experience in data engineering or related roles.

3 years of handson experience working with cloud data platforms (AWS and/or Azure).

Experience supporting enterprisescale data analytics and AI solutions.

Preferred Qualifications

Cloud certifications (AWS Azure Databricks Snowflake).

Experience in healthcare life sciences finance or other regulated industries.

Exposure to realtime or streaming data architectures.

Experience with data governance metadata tools and privacy frameworks.