Senior Data Engineer (Permanent WFH)

Job Specification

● 6+ years of professional experience in data engineering, analytics engineering, or a

closely related technical data role.

● Strong hands-on experience working with AWS, Snowflake, and Databricks, including

managing, transforming, and optimizing enterprise data within these environments.

● Advanced SQL proficiency, including complex transformations, query optimization,

performance tuning, and data modeling.

● Strong Python programming skills for data engineering, scripting, automation, and

pipeline development.

● Demonstrated experience designing, developing, and maintaining production-grade

ETL/ELT pipelines and data integration workflows.

● Experience with modern data modeling and transformation frameworks, including dbt.

● Solid understanding of cloud data architecture, data warehousing, and scalable data

processing methodologies.

● Experience implementing data quality, monitoring, and operational reliability practices

within enterprise data environments.

● Ability to work independently, troubleshoot complex technical challenges, and

collaborate effectively with technical and business stakeholders.

Preferred Qualifications

● Experience with AWS data services such as S3, Glue, Lambda, Redshift, and related

data integration technologies.

● Experience with Databricks, Apache Spark, Delta Lake, and distributed data processing.

● Advanced Snowflake experience, including warehouse optimization, data sharing,

security, and performance management.

● Familiarity with workflow orchestration tools such as Apache Airflow or similar platforms.

● Experience with CI/CD pipelines, Git-based version control, and infrastructure-as-code

practices.

● Experience supporting enterprise BI platforms such as Tableau and developing certified

analytical data sources.

● Familiarity with data governance frameworks, data catalogs, lineage tracking, RBAC,

and PII masking.

● Experience preparing and managing data for machine learning, AI, and vector database

applications.

● Experience integrating data from enterprise CRM, ERP, CSP, and SaaS platforms.

Key Responsibilities

● Design, develop, and maintain scalable data pipelines and ETL/ELT workflows across

AWS, Snowflake, and Databricks.

● Manage and optimize enterprise data storage, processing, and transformation within

cloud-based data platforms, ensuring performance, reliability, and cost efficiency.

● Develop and maintain advanced SQL and Python-based data transformations to support

analytical workloads, reporting, and downstream applications.

● Design and implement robust data models using dbt and modern data engineering

practices to deliver reliable, reusable, and high-performance data products.

● Build and maintain data ingestion and integration processes connecting enterprise

systems, including CRM, ERP, cloud platforms, and product applications.

● Implement automated data quality checks, monitoring, validation, and orchestration to

ensure data accuracy, consistency, and availability.

● Optimize Snowflake warehouses, Databricks workloads, and AWS-based data services

for efficient processing of large-scale datasets.

● Collaborate with analytics and business intelligence teams to support enterprise

reporting, semantic models, and certified data sources.

● Support data governance initiatives, including data lineage, cataloging, Role-Based

Access Control (RBAC), and Personally Identifiable Information (PII) protection.

● Partner with cross-functional stakeholders across GTM, Finance, Product, CX, Legal,

and People teams to translate complex business requirements into scalable technical

data solutions.

● Support data infrastructure and architecture requirements for machine learning, AI, and

advanced analytics applications