Data Engineer

• Evolving data infrastructure and data analytics capabilities
• Building architecturally-sound data systems and pipelines for analytics and data science
• Leading full stack development projects
• Collaborating with cross-functional teams
• Managing data logistics including specifications, transfers, and structures
• Accessing, extracting, and transforming data from various sources using tools like Hadoop, Spark, and SQL
• Supporting analytical proof of concepts and tool exploration
• Developing scalable ETL pipelines and ensuring data quality and reliability
• Assisting in Agile/Lean methodologies and DevOps practices

• Engage in planning, analysis, design, and development of data management, data integration, data warehouse, and business intelligence solutions.
• Engage in discussions with the team, clients, and project sponsors to determine project goals and set priorities.
• Convert business and technical requirements into detailed data analysis and system design specifications.
• Utilize database, programming, and ETL tools to implement and maintain data management and warehousing solutions, creating valuable data analytics assets for clients.
• Work closely with clients and the BerryDunn team to analyze systems, gather requirements, map data pipelines, and assess data quality.
• Conduct data quality analysis, investigate and resolve issues to ensure data integrity and accuracy.
• Create and deliver reports and analytic applications using suitable business intelligence or custom tools to meet client needs.
• Improve efficiency and reliability of client and BerryDunn data processes through innovative solutions and best practices.
• Use Artificial Intelligence tools to enhance productivity and improve project deliverables.
• Collaborate with other groups within BerryDunn to provide support for data and system analysis, data pipeline engineering, and data architecture.
• Provide guidance and mentorship to junior team members, fostering a culture of continuous learning and improvement.
• Keep abreast of the latest trends and technologies in data engineering, healthcare analytics, and data science to ensure BerryDunn remains at the forefront of the industry.
• Ensure all data solutions comply with industry standards, regulations, and security protocols, particularly those specific to healthcare.

Senior Healthcare Data Engineer

• Design, build and maintain data systems and pipelines
• Ensure data is accessed and utilized optimally for analytics or visualization/insight generation
• Build and execute data engineering approaches to support business priorities
• Integrate varying sizes of data from different sources (including Snowflake, Fineos, Web API and Teradata)
• Create data assets that conform to scalability, extensibility, performance and maintainability requirements for the problem at hand
• Understand and contribute to the evolution of the enterprise data architecture
• Support integration of solutions within existing business processes using automation techniques
• Provide support to lower level Data Engineer peers

Data Engineer I

• Manage a team by hiring and onboarding talent, setting clear expectations, conducting formal performance reviews, and coaching accountability.
• Lead the team to deliver on the enterprise data platform roadmap by translating architecture direction into sequenced execution plans, team backlogs, and measurable outcomes.
• Create a strong engineering culture across the team by setting standards for maintainability, organization, and repeatability; growing engineers via regular 1:1s, timely feedback, development plans, and formal review cycles; and being responsible for overall team performance and cohesion.
• Own day-to-day execution for the Data Engineering team by breaking work into tasks, driving sprint-level planning, delegating effectively to grow ownership, unblocking delivery, and ensuring high-quality outcomes through engineering reviews and feedback.
• Drive implementation of the Medallion architecture (Bronze → Silver → Gold) with strong enforcement of layer responsibilities, ensuring repeatable and auditable pipeline behavior leveraging Boomi, Dbt, Azure Data Bricks, Boomi/Rivery, Delta tables, and Sql Server.
• Partner closely with the Data Architect to bring the architecture to life through concrete implementation patterns, reference pipelines, and guardrails—ensuring the team can consistently execute the intended approach.
• Reduce friction by driving crisp decisions and forward motion—using lightweight decision records, seeking the right input, and then executing accountability (while avoiding overly heavy process).
• Establish DataOps practices aligned with modern software development lifecycle (SDLC): CI/CD for data assets, consistent branching/release patterns, code review standards, and runbooks/operational readiness for pipelines.
• Improve pipeline observability and operational reliability by implementing monitoring for freshness/staleness, failure modes, and quality signals, and ensuring the team responds predictably to incidents and support requests.
• Drive stakeholder partnership and intake stream: collaborate with business partners to clarify requirements, reduce ad-hoc thrash, and shape requests into deliverable, well-scoped work that fits the platform strategy.
• Develop future leaders and succession plans for key roles through assigning regularly occurring responsibilities, mentoring, and strategic delegation.
• Support and promote the company values through positive interactions with both internal and external stakeholders on a regular basis.
• Other strategic business initiatives or special cross-functional project involvement as required.

Manager, Data Engineering

• Build Pipelines: Design and maintain scalable data pipelines to ingest and enrich healthcare data using Databricks and Spark.
• Optimize Data Workflows: Improve data intake processes and optimize SparkSQL/Python workloads for performance, scalability, and cost efficiency.
• Design Data Models: Partner with senior engineers to develop data marts and semantic models that power analytics products and reporting.
• Ensure Quality: Monitor pipeline health, troubleshoot failures, and implement data validation and quality controls.
• Learn & Grow: Expand your knowledge of the full data lifecycle, cloud infrastructure (Azure/AWS), and healthcare data standards.

Data Engineer – Entry Level

• Own End-to-End Delivery of Core Data Platform Components
• Design and ship the data normalization, schema mapping, validation, enrichment, and distribution pipeline for a net-new intelligent data warehouse
• Write production code as a hands-on individual contributor. This is not a role that delegates implementation to others
• Take technical ownership from architecture through deployment, with accountability for reliability, performance, and correctness
• Drive Technical Architecture
• Partner with a small seed team of senior engineers to define the end-to-end architecture for an AI-native data warehouse serving institutional financial clients
• Bring opinionated decisions on schema design, normalization strategies, API exposure patterns, and data distribution approaches
• Evaluate and select technologies with a bias toward what ships well and scales sustainably
• Build AI Evaluation Infrastructure
• Design and implement the evaluation framework that makes AI-generated outputs trustworthy in high-stakes financial data contexts
• Build cross-model comparison tooling, deterministic validation checks, and human-in-the-loop review workflows
• Contribute to shared AI infrastructure that will serve as a foundation for multiple products across the company
• Ship with AI-Native Development Practices
• Use agentic coding tools and LLM-assisted development as your primary workflow; this is how the entire team operates
• Bring strong opinions about how to get the most out of AI-assisted development while maintaining quality and reliability
• Contribute to the team’s evolving practices around AI-accelerated SDLC
• Establish Technical Standards
• As an early senior voice on a new team, set coding standards, review practices, and architectural documentation that will scale
• Help define what “good” looks like for a team building at speed without sacrificing quality
• Mentor and provide technical guidance as the team grows from seed squad to full delivery organization
• Collaborate Across Engineering and Product
• Work closely with your engineering manager, peer engineers, and product management to translate requirements into executable technical plans
• Participate actively in design reviews and roadmap discussions with grounded, implementation-level perspective
• Engage directly with the nuances of private markets data – fund administration, investment data schemas, institutional reporting workflows – to build domain intuition that improves your technical decisions

Staff Software Engineer, Data Platform

• You will work closely with finance, accounting, billing, and revenue teams to understand their main pain points and translate them into self-serve, reliable, and scalable data solutions
• You are expected to mentor other team members and be a champion of engineering excellence across the organization
• You will be part of a small team with a large amount of ownership and autonomy, managing initiatives directly from design through execution
• You will have the freedom to suggest and drive organization-wide initiatives that shape the financial data vision and roadmap
• You will own critical data integration pipelines and models, ensuring uniform, reliable, timely, and accurate financial reporting

Senior Data Engineer, Finance

• Design the vision and blueprint of an organization’s data framework.
• Design and manage data systems, set policies for how data is stored and accessed, coordinate various data sources within an organization, and integrate new data technologies into existing IT infrastructures.
• Data modeling and design, including software development and database administration.

Data Architect – Data Ecosystems

• The Data Warehouse Engineer is a hands-on role responsible for designing, building, and operating Maverick’s Azure data platform.
• Reporting to the Data Team Director, you will partner with external implementation resources in Phase 1 and then own daily operations, reliability, and continuous improvement of the platform.
• Monitor health and SLAs via Azure Monitor/Log Analytics, Synapse and Data Factory dashboards; respond to critical pipeline incidents within 2 hours.
• Triage and resolve data integration failures (sponsor bank files, CRM/ITSM connectors, API ingestions), distinguishing data quality vs. pipeline vs. infrastructure issues; escalate when needed.
• Perform root-cause analysis (RCA) and drive corrective actions; maintain and conduct post-mortems.
• Design and build Azure Data Factory pipelines (ETL/ELT) for batch and incremental ingestion using parameterized, reusable templates.

Data Warehouse Engineer

• Joining each and every one of your colleagues in creating an inclusive workspace;
• Owning, maintaining, and supporting the reliability, architecture, and evolution of our production machine learning and delivery platform;
• Designing, operating, and improving large scale Databricks/Spark pipelines and workflows
• Partner with our data scientists to productionize new data and ML-powered products;
• Leading improvements on the software integration layers between data pipelines and product workflows;
• Growing the team via mentorship and leadership, reviewing technical documents, improving code quality, and improving team performance and processes;
• Acting as a senior technical owner in debugging complex production issues and improving system observability;
• Collaborating with cross-functional teams to support their data infrastructure needs.

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