Senior Data Engineer

Huxley; Boston (MA); On-site; USD 140,000 - 180,000; Full time

Job summary

Huxley in Boston seeks a Senior Data Engineer to design, build, and optimize cloud-based data platforms that deliver reliable analytics and AI-ready insights. You will shape data architectures, automate pipelines, and develop analytical models to enable reporting and self-serve BI.

The role requires strong SQL, PostgreSQL, and cloud data services experience, plus collaboration with stakeholders across product and engineering to ensure data is accurate and scalable.

Qualifications

• Bachelor's degree or equivalent practical experience in CS/IS/Engineering or related field.
• Proven experience designing and supporting enterprise-scale data platforms.
• Strong SQL expertise and data warehouse design.
• Experience with relational DBs like PostgreSQL, SQL Server, MySQL, or Oracle.
• Experience building large-scale data ingestion and transformation pipelines.
• Knowledge of distributed processing frameworks (e.g., Apache Spark).
• Experience with cloud data services, orchestration tools, storage, and CI/CD.
• Python programming for automation and data integration.
• Excellent communication and collaboration.

Responsibilities

• Design, build, and optimize cloud-based data platforms for analytics and AI.
• Develop automated data ingestion and transformation pipelines.
• Create governed semantic layers and scalable data models.
• Tune performance and manage costs.
• Collaborate with stakeholders and teams.

Skills

SQL

Python

Data modeling

Cloud platforms

Data pipelines

Education

Bachelor's degree in CS/IS/Engineering

Tools

PostgreSQL

Apache Spark

Airflow

CI/CD

Job description

Senior Data Engineer | Snowflake | SQL | PostgreSQL | Boston | Finance | FinTech

Location

Boston, MA (3 days onsite)

We are seeking an experienced Senior Data Engineer to design, build, and optimize modern cloud-based data platforms that deliver reliable, scalable, and business-critical insights. In this role, you will develop data architectures, automated pipelines, and analytical data models that enable reporting, advanced analytics, and AI-driven solutions.

You will work closely with business stakeholders, data analysts, product teams, and technology professionals to ensure data is accessible, trusted, and optimized for performance. This position requires strong technical expertise across cloud data platforms, data engineering best practices, and modern data architecture principles.

Data Architecture & Engineering

• Design, implement, and support scalable cloud-based data platforms that support analytics, reporting, application integration, and AI use cases.
• Evaluate and recommend the most appropriate technologies based on business requirements, performance needs, scalability, and cost efficiency.
• Develop and maintain high-volume, automated data ingestion and transformation pipelines.
• Integrate data from a variety of internal and external sources including APIs, databases, event streams, and flat-file exports.
• Implement reliable incremental loading, change data capture, and historical backfill strategies.

Data Modeling & Analytics

• Design and maintain enterprise data models, including dimensional models, fact and dimension tables, and slowly changing dimension (SCD) structures.
• Create and manage curated analytical datasets that support self-service analytics and business intelligence.
• Develop governed semantic layers and business-friendly data models to ensure consistent reporting and metric definitions across platforms.
• Support AI-enabled data experiences, including semantic search, retrieval-augmented generation (RAG), and natural language data access.

Performance & Optimization

• Optimize database and warehouse performance through query tuning, workload management, indexing, clustering, and resource optimization techniques.
• Monitor and manage platform costs while maintaining performance and scalability.
• Establish monitoring, alerting, troubleshooting, and incident response processes for production data environments.

Data Governance & Quality

• Develop and maintain data quality frameworks including data validation, reconciliation, anomaly detection, and completeness checks.
• Ensure adherence to data governance, lineage, security, and compliance standards.
• Maintain technical documentation, operational procedures, and architecture diagrams.
• Collaborate with business and technical stakeholders to gather requirements and translate them into scalable technical solutions.
• Participate in agile delivery practices, technical reviews, and continuous improvement initiatives.
• Contribute to knowledge sharing, mentoring, and engineering best practices across the team.

Required Qualifications

• Bachelor's degree in Computer Science, Information Systems, Engineering, Data Science, or a related field, or equivalent practical experience.
• Proven experience designing and supporting enterprise-scale data platforms and analytical solutions.
• Strong expertise in SQL, including complex queries, performance optimization, and data warehouse design.
• Extensive experience with relational database technologies such as SQL Server, PostgreSQL, MySQL, or Oracle.
• Experience building and maintaining large-scale data ingestion and transformation pipelines.
• Knowledge of distributed data processing frameworks such as Apache Spark or similar technologies.
• Hands-on experience with cloud-based data services, orchestration tools, storage platforms, and CI/CD processes.
• Strong understanding of dimensional modeling, data warehousing concepts, and curated analytical datasets.
• Experience working with modern data formats such as Parquet, Avro, and JSON.
• Knowledge of data governance, metadata management, lineage, and data quality best practices.
• Experience applying AI and machine learning concepts within data platforms, including semantic models and retrieval-based solutions.
• Strong programming skills in Python for automation, orchestration, and data integration.
• Excellent verbal and written communication skills with the ability to engage both technical and non-technical audiences.
• Demonstrated problem-solving abilities, ownership mindset, and commitment to continuous improvement.

Preferred Experience

• Experience with modern cloud data warehouses and lakehouse architectures.
• Exposure to AI-powered analytics, semantic data models, and natural language data solutions.
• Experience in highly regulated, data-intensive, or enterprise-scale environments.
• Familiarity with DevOps and Infrastructure-as-Code practices.

This role is ideal for a data engineering professional who enjoys building scalable data ecosystems, solving complex data challenges, and enabling advanced analytics and AI capabilities across the organization.