Company
SeniorDataEngineer
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“Senior Data Engineer. Skills: Data Engineering, Cloud Data Platforms, Data Pipelines, ETL/ELT. Design data pipelines. Develop data pipelines”
Industry & Context.
Problem-solving; Analytical skills
On-call rotation
What They're Looking For.
Must Have
5+ years of experience in data engineering, 3+ years of hands-on experience working with cloud data platforms, Experience supporting enterprise-scale data, analytics, and AI solutions, Experience with Python and SQL, Hands-on 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, 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, Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Science, or a related field
Nice to Have
Master's degree is a plus, Cloud certifications (AWS, Azure, Databricks, Snowflake), Experience in healthcare, life sciences, finance, or other regulated industries, Exposure to real-time or streaming data architectures, Experience with data governance, metadata tools, and privacy frameworks
What You'll Do.
Design data pipelines
Develop data pipelines
Optimize data pipelines
Build ETL/ELT workflows
Implement data transformations
Orchestrate data using Databricks
Orchestrate data using SQL
Orchestrate data using Python
Implement data solutions on AWS
Implement data solutions on Azure
Design cloud storage solutions
Manage cloud storage solutions
Build cloud-agnostic architectures
Build hybrid architectures
Optimize performance across platforms
Optimize scalability across platforms
Optimize reliability across platforms
Optimize cost across platforms
Design Snowflake data warehouse solutions
Support Snowflake data warehouse solutions
Enable self-service analytics
Ensure data availability
Prepare features for SageMaker
Prepare features for Amazon Bedrock
Engineer features for SageMaker
Engineer features for Amazon Bedrock
Collaborate with data scientists
Operationalize ML models
Enable GenAI workloads
Enable advanced analytics workloads
Implement data quality checks
Implement monitoring frameworks
Implement validation frameworks
Ensure compliance with security requirements
Ensure compliance with privacy requirements
Ensure compliance with regulatory requirements
Apply data governance standards
Manage access controls
Implement CI/CD pipelines
Participate in on-call rotations
Work closely with product owners
Work closely with business stakeholders
Work closely with architects
Work closely with analytics teams
Translate business requirements into technical solutions
Contribute to documentation
Contribute to standards
Contribute to best practices
How You'll Work.
Team & Collaboration
Collaborates with data scientists; Collaborates with analytics teams; Collaborates with business stakeholders; Collaborates with product owners; Collaborates with architects; Agile teams
Communication Scope
Communicate technical concepts
Process & Methodology
Agile
Full Job Description
**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 cloud‑agnostic or hybrid architectures supporting multi‑cloud 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 self‑service analytics and BI use cases through curated, high‑quality datasets. Partner with reporting and analytics teams to ensure data availability and accuracy. _AI / ML & Advanced Analytics Enablemen_t 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 complianc
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