Senior Software Engineer, Data (L3)

Senior Software Engineer, Data (L3)

Austin, TX (Onsite 4 days per week)

Note: This is a full-time role and we do not offer C2C or C2H employment and are not able to sponsor visas for this position.

About Acrisure

Acrisure Enterprise Technology Group is where human expertise meets advanced technology.

A global fintech leader, Acrisure empowers millions of ambitious businesses and individuals with the right solutions to grow boldly forward. Bringing cutting-edge technology and top-tier human support together, we connect clients with customized solutions across a range of insurance, reinsurance, payroll, benefits, cybersecurity, mortgage services – and more.

In the last twelve years, Acrisure has grown in revenue from $38 million to almost $5 billion and employs over 19,000 colleagues in more than 20 countries. Acrisure was built on entrepreneurial spirit. Prioritizing leadership, accountability, and collaboration, we equip our teams to work at the highest levels possible.

Job Summary:

The Enterprise Data team’s mission is to unify data across the enterprise to optimize business decisions made at the strategic, tactical, and operational levels of the organization. We accom-plish this by building a data foundation utilizing Palantir Foundry, AIP and Ontology that powers analytics and reporting platforms, and business processes that provide quality data, in a timely fashion, from any channel of the company and present them in such a manner as to maximize the value of that data for both internal and external customers.

The Senior Software Engineer, Data Engineer is responsible for building technology and pipe-lines to rapidly model different and varying data sources into reusable data models that can be consumed by a wide variety of customers including operational applications as well as analytics & metrics. Responsibilities include hands-on contributions in a team environment, critical think-ing and developing technical solutions with business stakeholders and changing requirements, and mentoring engineers to ensure high-quality development and best practices are maintained throughout the delivery cycles.

Our Data platform is transitioning to Palantir from a Google and Microsoft hybrid cloud tech stack. Our data storage layer includes BigQuery, Databricks, Azure, and Postgres. Across our broader tech stack, we code primarily in Python, Scala, and JavaScript and make use of many frameworks including Dataflow, Apache Airflow, Apache Spark, dbt, Delta Lake, Apache Iceberg.

Here are some of the ways in which you’ll achieve impact:

• •Build and evolve reusable data engineering patterns that enable the rapid, scalable, and consistent onboarding of new data sources into the enterprise lakehouse.
• •Design and implement reliable, automated data management processes that improve data quality, security, governance, and the responsible handling of sensitive information.
• •Own major technical deliverables end to end , from solution design and development through testing, deployment, monitoring, and production support, while ensuring solutions meet business requirements and engineering standards.
• •Design and build reusable data models, pipelines, frameworks, and components that can support multiple business use cases and improve development velocity across the team.
• •Identify opportunities to improve performance, reliability, scalability, and maintainability , including addressing technical debt and establishing better engineering patterns and standards.
• •Apply disciplined engineering practices , including automated testing, data validation, CI/CD, observability, code reviews, and documentation, to deliver high-quality and production-ready data solutions.
• •Collaborate closely with Product and business stakeholders to understand priorities, translate business requirements and KPIs into technical solutions, and adapt effectively as requirements evolve.
• •Partner with Data, AI, and other engineering teams to provide trusted, well-modeled, and accessible data that can power analytics, AI applications, operational workflows, and enterprise business processes.
• •Lead through technical expertise and collaboration , contributing to design discussions, guiding technical decisions, mentoring other engineers, and sharing patterns, learnings, and best practices across the team.
• •Proactively identify gaps and opportunities in the data platform, tooling, and engineering processes, and drive practical improvements that increase the team's overall execution and impact.

You may be fit for this role if you have:

• •8–10+ years of experience in software or data engineering , with experience building and supporting enterprise data platforms, data warehouses, lakehouses, or data-intensive applications.
• •Good hands-on data engineering experience , with strong SQL skills and proficiency in Python, Scala, or Java. You should be comfortable building complex data pipelines, transformations, reusable components, and data services.
• •Good understanding of modern lakehouse architecture , including Delta Lake, Apache Iceberg, data modeling, partitioning, schema evolution, incremental processing, CDC, data quality, and performance tuning.
• •Strong experience with Databricks , including Delta tables, Spark, batch and streaming pipelines, performance optimization, and scalable data engineering practices.
• •Experience working with cloud data platforms , especially GCP and/or Azure. Experience with technologies such as BigQuery, Azure, Databricks, Postgres, Dataflow, or similar platforms is valuable.
• •Experience with data orchestration and integration tools , such as Airflow, Dagster, Fivetran, dbt, APIs, CDC, and other batch or streaming technologies.
• •Palantir Foundry, Ontology, or AIP experience is a strong plus, but not mandatory. We are also looking for strong data engineers who have good engineering fundamentals and are willing to learn and work with Palantir technologies.
• •Quick learner who can pick up new platforms and technologies , understand how they work, and use them effectively to solve data engineering problems.
• •Good software engineering fundamentals , including modular design, version control, code reviews, automated testing, CI/CD, observability, troubleshooting, and production support.
• •Hands-on experience with automation and testing , including data quality checks, validation frameworks, reconciliation, automated testing, and tools that make data pipelines more reliable and easier to support.
• •Experience building reusable data structures and engineering patterns that can be used across different teams and use cases, while improving scalability, maintainability, and development speed.
• •Good problem-solving and technical design skills , with the ability to understand existing systems, identify gaps, evaluate tradeoffs, reduce technical debt, and come up with practical solutions.
• •Experience working with data at scale , including identifying performance bottlenecks and improving query performance, pipeline processing, storage, and overall reliability.
• •Comfortable working directly with business and product teams , understanding their requirements and KPIs, and translating them into practical and scalable technical solutions.
• •Ownership mindset , with the ability to take a complex problem from design through development, testing, deployment, and production support.
• •Good communication skills , with the ability to explain technical concepts clearly, document designs, participate in technical discussions, and work effectively with engineers, product managers, data scientists, analysts, and business teams.
• •Willingness to mentor and support other engineers , including helping with design decisions, code reviews, technical discussions, and sharing good engineering practices.
• •Experience working across different business domains , with an understanding of how data products and platforms support strategic, tactical, and day-to-day business decisions.
• •Cont