Senior data engineer, analytics and ETL

A senior data engineer is needed to support a next‑generation data and analytics initiative. This role focuses on designing and maintaining enterprise‑grade ETL processes, building scalable analytics solutions, and enabling high‑quality reporting and dashboards. The position requires deep experience across data integration, modeling, cloud data platforms, and analytics tooling.

This is a senior‑level role intended for an experienced professional who can operate independently, contribute to architecture decisions, and collaborate closely with technical and business stakeholders. The role follows a hybrid schedule with in‑office collaboration Tuesday through Thursday in Woburn, Massachusetts.

Responsibilities

• Design, develop, and optimize ETL processes that meet project and enterprise data integration standards.
• Build, test, document, and maintain ETL workflows and programs.
• Perform data mapping from transactional source systems to data warehouse targets.
• Develop and maintain logical and physical data models using established best practices.
• Create and maintain interactive dashboards and reports using Power BI.
• Develop and support analytics solutions using Oracle Analytics Server.
• Implement data integration solutions using Microsoft Azure services, including Data Factory, Synapse, and SQL Database.
• Perform data validation, integrity checks, and performance tuning for data structures and pipelines.
• Maintain clear technical documentation and manage source code using standard version control tools.
• Monitor and apply emerging trends, tools, and best practices in ETL, analytics, and cloud data platforms.
• Provide guidance, training, and support to stakeholders on analytics tools and reporting solutions.
• Collaborate within agile, cross‑functional teams to deliver high‑quality data solutions.

Required experience and skills

• Eight or more years of professional experience in data engineering or analytics engineering roles.
• Strong hands‑on experience with ETL development and data integration.
• Proven expertise with Oracle Data Integrator and data mapping techniques.
• Hands‑on experience with Oracle Analytics Server and Power BI.
• Strong SQL skills with experience across transactional systems and data warehouses.
• Solid understanding of database design, data modeling, and data administration concepts.
• Experience working with Microsoft Azure data services, including Azure Data Factory, Synapse, and SQL Database.
• Strong analytical, problem‑solving, and communication skills.
• Experience working in agile, collaborative team environments.

FAQ

1. What are the core responsibilities of a Senior Data Engineer focused on analytics and ETL?
This role designs, builds, and optimizes ETL/ELT pipelines that transform raw data into analytics-ready datasets. It involves integrating multiple data sources, ensuring data quality, and supporting reporting and business intelligence needs. The engineer also contributes to data architecture and mentors junior team members.

2. What types of data pipelines are typically built in this role?
Pipelines include batch and real-time data processing workflows that ingest, transform, and load data into warehouses or data lakes. These pipelines often support dashboards, reporting systems, and advanced analytics use cases. Scalability and reliability are key design considerations.

3. What tools and technologies are commonly used?
Common tools include SQL and Python for data transformation and scripting. ETL frameworks, orchestration tools like Airflow, and big data tools such as Spark are frequently used. Cloud platforms and data warehouses like Snowflake, Redshift, or BigQuery are also common.

4. How is data quality ensured in ETL processes?
Data quality is maintained through validation checks, automated testing, and monitoring pipelines. The engineer implements rules to ensure accuracy, completeness, and consistency. Alerts and logging help identify and resolve issues quickly.

5. How does this role support analytics and business intelligence teams?
The engineer provides clean, structured, and reliable datasets for analysts and data scientists. Collaboration ensures that data models meet reporting and analytical requirements. This support enables faster and more accurate decision-making.