Senior Data Engineer

Harnham; United States; On-site; USD 120,000 - 180,000; Full time

Job summary

Harnham is seeking a hands-on Senior Data Engineer to join a growing Data Engineering team. You will design and build modern cloud-based data pipelines to support analytics, reporting, AI, and business decision-making.

You will work on large-scale data challenges, with a focus on trusted, scalable datasets, data quality, and platform reliability in a collaborative environment.

Qualifications

• 5+ years of experience in Data Engineering or related field.
• Strong Python programming skills with data frameworks and APIs.
• Advanced SQL skills with complex transformations.

Responsibilities

• Design, develop, and maintain scalable data pipelines using Python, SQL, and cloud-native technologies.
• Ingest and integrate data from APIs, SaaS applications, databases, and external data providers.
• Build and optimize ELT/ETL workflows into a Snowflake data platform.

Skills

Python

SQL

Snowflake

ELT/ETL

Airflow

Cloud platforms

Data modeling

Data quality/observability

Production pipelines

Tools

Databricks

Kafka

Event Hubs

dbt

Informatica IDMC

Job description

We are seeking a hands-on Senior Data Engineer to join a growing Data Engineering team focused on building modern, cloud-based data platforms that power analytics, reporting, AI, and business decision-making.

This is an opportunity to work on large-scale data challenges within a company undergoing significant investment in data modernization and AI initiatives. You'll help design and build data pipelines, integrate new data sources, improve data quality, and enable downstream analytics teams with trusted, scalable datasets.

The ideal candidate enjoys solving data movement problems, building production-grade pipelines, and working across a modern data stack in a fast-moving, highly collaborative environment.

What You'll Do

• Design, develop, and maintain scalable data pipelines using Python, SQL, and cloud-native technologies.
• Ingest and integrate data from APIs, SaaS applications, enterprise systems, databases, and external data providers.
• Build and optimize ELT/ETL workflows that move data into a centralized Snowflake data platform.
• Develop and maintain bronze and silver layer datasets within a medallion architecture framework.
• Implement data quality checks, validation rules, monitoring, and observability processes.
• Troubleshoot production issues, investigate data discrepancies, and support platform reliability.
• Partner closely with Analytics Engineers, Data Scientists, Product teams, and business stakeholders to deliver trusted datasets.
• Contribute to data governance, metadata management, and platform best practices.
• Optimize pipeline performance, scalability, and operational efficiency.
• Drive continuous improvement in DataOps, automation, and engineering standards.

Required Qualifications

• 5+ years of experience in Data Engineering or a related field.
• Strong Python programming skills, including experience working with data frameworks and APIs.
• Advanced SQL skills with experience building complex transformations and analytical datasets.
• Hands-on experience with Snowflake and modern cloud data platforms.
• Experience designing and maintaining ELT/ETL pipelines in production environments.
• Experience with orchestration tools such as Airflow.
• Experience working with cloud platforms such as Azure, AWS, or GCP.
• Strong understanding of data modeling, dimensional modeling, and data warehousing concepts.
• Experience implementing data quality, monitoring, and observability practices.
• Ability to support and troubleshoot production data pipelines.

Preferred Qualifications

• Experience with Databricks, Kafka, Event Hubs, or other streaming technologies.
• Experience with dbt and modern data transformation frameworks.
• Familiarity with CDC, incremental processing, and real-time data architectures.
• Experience working within a medallion architecture (Bronze/Silver/Gold).
• Exposure to Informatica IDMC or similar enterprise data integration platforms.
• Experience supporting AI, machine learning, or advanced analytics initiatives.
• Knowledge of data governance, lineage, privacy, and compliance frameworks.
• Experience working with large-scale transactional or customer data environments.