PA2026Q1JB031 -Senior Data Engineer

As a leading financial services and healthcare technology company based on revenue, SS&C is headquartered in Windsor, Connecticut, and has 27,000+ employees in 35 countries. Some 20,000 financial services and healthcare organizations, from the world's largest companies to small and mid-market firms, rely on SS&C for expertise, scale, and technology.

Job Description

Job Summary

We are seeking an experienced Senior Data Engineer to build, optimize, and maintain scalable data pipelines and infrastructure in a modern lakehouse environment. You will work closely with Data Architects to implement well-defined data products, schemas, and patterns, ensuring reliable data ingestion, transformation, quality, and distribution. This role requires strong hands-on expertise with both batch and streaming systems, as well as a deep focus on performance, reliability, and operational excellence.

Key Responsibilities

• Implement and maintain end-to-end data pipelines for data acquisition from diverse sources, including databases, APIs, files, and messaging systems such as Kafka.
• Build robust data validation, enrichment, and transformation workflows using Python and pySpark.
• Develop and optimize data storage and querying layers using technologies such as Apache Iceberg, Trino, StarRocks, and Snowflake.
• Implement and maintain dimensional data models, including Star and Snowflake schemas, as defined by data architecture standards.
• Integrate and manage streaming data flows using Kafka for both ingestion and real-time data distribution.
• Design and implement data quality checks, monitoring, and alerting to ensure high data reliability.
• Contribute to metadata management, data governance, and security practices, including access controls and data masking.
• Enable data distribution and consumption through files, APIs, Kafka, Snowflake data sharing, and analytics tools.
• Optimize pipeline performance, cost, and scalability while troubleshooting and resolving production issues.
• Collaborate closely with data architects, analysts, data scientists, and stakeholders to deliver high-quality data products.
• Mentor junior engineers and promote best practices in code quality, testing, and CI/CD for data pipelines.
  
  

Required Skills And Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• 5+ years of hands-on experience in data engineering roles, including at least 2 years working with big data or lakehouse platforms.
• Strong proficiency in Python and pySpark for building scalable data processing pipelines.
• Hands-on experience with analytical and query platforms such as Trino, StarRocks, and Snowflake.
• Experience working with open table formats, particularly Apache Iceberg.
• Proven experience with streaming technologies, especially Apache Kafka.
• Solid understanding of dimensional modeling and data warehousing concepts.
• Familiarity with data quality frameworks, metadata management, governance tools, and security best practices.
• Experience with cloud platforms such as AWS, Azure, or GCP, and infrastructure-as-code tools.
• Strong problem-solving skills with experience debugging and tuning complex data pipelines.
• Excellent communication and collaboration skills.
  
  

Preferred Qualifications

• Experience building and operating large-scale real-time and batch data platforms.
• Familiarity with orchestration tools such as Airflow or Dagster.
• Experience with CI/CD practices for data engineering workflows.
• Familiarity with BI tools and analytic dashboard integrations.
• Relevant certifications (e.g., Databricks, Snowflake, Confluent) or contributions to open-source projects.
  
  

Unless explicitly requested or approached by SS&C Technologies, Inc. or any of its affiliated companies, the company will not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services.

SS&C Technologies is an Equal Employment Opportunity employer and does not discriminate against any applicant for employment or employee on the basis of race, color, religious creed, gender, age, marital status, sexual orientation, national origin, disability, veteran status or any other classification protected by applicable discrimination laws.