Senior Data Engineer

Partner with business stakeholders to understand data requirements and translate them into scalable technical solutions that drive operational efficiency and strategic insights

Lead data innovation initiatives by identifying opportunities to leverage data assets for new business capabilities and competitive advantages

Review internal and external business and product requirements for data operations and recommend strategic changes and upgrades to systems and storage

Collaborate with data scientists to enable advanced analytics, predictive modeling, and machine learning initiatives that solve complex business problems

Work with Professional Services teams on client-focused data solutions, ensuring alignment with business objectives and customer needs

Design and oversee the deployment of comprehensive data architecture that captures, manages, and stores structured and unstructured data from multiple internal and external sources

Build resilient ETL/ELT pipelines that channel data from multiple inputs, route appropriately, and store using cloud structures, local databases, and other applicable storage forms

Establish processes and structures based on business and technical requirements to ensure optimal data flow across systems

Create and maintain well-documented data services and interfaces for efficient data access across the organization

Develop company-wide, web-enabled solutions that democratize data access and empower self-service analytics

Develop technical tools and programming leveraging artificial intelligence, machine learning, and big-data techniques to cleanse, organize, and transform data on an automated basis

Implement comprehensive data quality frameworks including validation checks, monitoring, and automated recovery strategies to maintain data accuracy, completeness, and freshness

Apply business logic to cleanse, enrich, and structure raw data, ensuring consistency and quality across domains

Leverage Model Context Protocol (MCP) to connect with top enterprise applications, enabling seamless automation of data flows and improving operational efficiency

Utilize Copilot and Anthropic models to accelerate development, automate documentation, and enhance code quality and review processes

Create and establish design standards and assurance processes for software, systems, and applications development to ensure compatibility and operability of data connections, flows, and storage requirements

Ensure secure, scalable, and auditable data ingestion processes, with appropriate handling of PII and compliance requirements

Uphold SDLC best practices across development and delivery stages to ensure reliability, maintainability, and scalability

Maintain and defend data structures and integrity on an automated basis, implementing proactive monitoring and alerting systems

Troubleshoot pipeline issues and collaborate with platform teams to optimize performance and recovery strategies

Participate in on-call rotations to ensure 24/7 reliability of critical data systems

Continuously evaluate and implement new technologies and methodologies to improve data engineering capabilities

Mentor junior team members and contribute to the growth of the data engineering practice

About you:

5+ years of hands-on experience in developing ETL/ELT pipelines across varied data sources, with demonstrated ability to work across the full spectrum of data engineering challenges

Experience with Copilot and Claude Anthropic models to enhance development speed, code quality, and documentation

Strong programming skills in languages such as Python, Scala, or Java, with ability to write production-quality code

Experience with modern data platforms and tools (e.g., Snowflake, Databricks, Apache Spark, Kafka, Airflow)

Practical knowledge of Model Context Protocol (MCP) to connect enterprise applications and automate data workflows

Experience with cloud platforms (AWS, Azure, GCP) and their native data services

Knowledge of containerization and orchestration technologies (Docker, Kubernetes)

Strong expertise in data integration, transformation, and curation with a focus on quality and consistency

Experience with real-time data processing and streaming architectures

Background in data science or analytics, with ability to collaborate effectively with data scientists

Experience in client-facing or Professional Services roles

Familiarity with DataOps and MLOps practices

A mindset focused on operational efficiency, automation, and continuous improvement

Strong business acumen with ability to translate technical capabilities into business value

Commitment to SDLC best practices and structured development processes

Excellent communication and collaboration skills, with ability to work effectively with both technical and non-technical stakeholders

Proactive approach to problem-solving with strong analytical and critical thinking skills

Passion for innovation and staying current with emerging technologies and industry trends

Proven experience with both structured and unstructured data, including design and implementation of solutions that leverage both traditional databases and modern cloud architectures

Experience managing sensitive data, including PII, with attention to compliance and governance requirements

Demonstrated ability to work with artificial intelligence, machine learning, and big-data techniques

Solid understanding of data modeling, data warehousing concepts, and dimensional modeling

#LI-Hybrid

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