Senior Data Engineer

Pontera is a fintech company on a mission to help people retire better. Our software platform enables retirement savers to get the help they need managing their 401(k) and other retirement plan accounts as part of a personalized strategy by their trusted financial advisor.

Pontera is used by financial advisors across the nation– from SMB to Fortune 500 RIA firms, independent broker-dealers, plan custodians, and plan advisors.

Backed by leading venture capital firms including ICONIQ Growth and Lightspeed Venture Partners, Pontera is built by talented individuals who share a dedication to helping people retire with greater security.

Our team is fast-growing and driven to become one of the largest fintech companies in the world. Our culture is built on a people-first principle: in a complex and numbers-driven industry, we never lose sight of the people we serve and work alongside. That’s where you come in.

We are seeking a highly skilled Senior Data Engineer to join our growing team. As a Senior Data Engineer, you will own the design, build, and optimization of Pontera’s data platform - powering analytics, and data-driven product features. At Pontera, the Data Engineer plays a crucial role in designing and overseeing all BI and analytics solutions. They collaborate closely with various departments such as Product, R&D, BizDev, and Executive teams to address their data analysis requirements. Additionally, they hold responsibility for essential financial and operational metrics.

This role offers the opportunity to make a significant impact in a fast-paced, high-growth environment while tackling meaningful challenges that directly advance our mission. At Pontera, we leverage a modern data stack, including Snowflake, DBT, Python, and Fivetran, built on infrastructure-as-code and CI/CD best practices, and increasingly explore AI-driven solutions to enhance data quality, automation, and insight generation.

Responsibilities

Design and maintain scalable data models and schemas in DBT/Snowflake to support analytical and operational workloads.

Build, optimize, and document robust ELT pipelines in SQL, Python and DBT for batch and near-real-time data ingestion.

Lead initiatives to explore and integrate AI-driven approaches that enhance data workflows, insights and end-users self service.

Implement automated data-quality testing, monitoring, and alerting to guarantee trustworthy and timely data.

Collaborate with Product and Engineering teams on event instrumentation, features analysis, schema design and data governance standards.

Partner with Analytics and Data Science to translate business questions into engineered data solutions.

Mentor junior engineers and analysts, championing best practices in coding, documentation, and peer review.

Tune warehouse performance and manage cost efficiency.

Requirements

Experience: 5+ years in a data-engineering role in the tech industry, with a proven track record of scaling data platforms in cloud environments (AWS, GCP, or Azure).

Technical Skills: Expert SQL; hands-on with Snowflake (or Redshift/BigQuery), DBT knowledge, Python knowledge.

Data Architecture: Deep understanding of dimensional modeling, CDC, and ELT best practices; experience integrating third-party APIs and event streams.

Quality & Reliability: Demonstrated success implementing data-quality frameworks and building highly reliable pipelines.

Business Acumen: Experience in FinTech, SaaS, or high-growth startup environments is a plus.

Communication: Excellent written and verbal communication skills, able to explain complex data concepts to both technical and non-technical audiences.

Collaboration: Strong interpersonal skills and the ability to work cross-functionally across Product, Engineering, and Analytics teams, team player.

Advantages

• Experience with streaming platforms (Kafka, Kinesis, Pub/Sub).
• Orchestration tools such as Airflow; familiarity with CI/CD, Docker, and infrastructure-as-code (e.g., Terraform)
• Familiarity with using AI-driven approaches within data engineering contexts
• Background in ML feature-store design or real-time data serving.
• Familiarity with BI tools (Looker, Tableau) from a data-engineering perspective.
  
  

• Opportunity: Have a major impact at a fast-growing startup that is revolutionizing the FinTech industry
• Team Culture: A collegial, collaborative, fun work environment with frequent team events
• Equity: All new hires are eligible for equity grant participation
• Professional Development: Sponsored learning & development programs
• Work Flexibility: A hybrid office work model (In-Office Mon/Tues/Weds and WFH Sun//Thurs)