Epicor Kinetic consultant leading master, transaction, and historical data migrations. Supporting a global industrial equipment manufacturer’s post - merger platform standardization.
PG
Senior Associate Data Engineering – Azure, Databricks
Posted 21 hours ago
About the role
- Senior Associate Data Engineer building Azure and Databricks pipelines for Publicis Sapient’s digital transformation clients. Supporting AI-enabled data services, retrieval systems, and scalable cloud platforms.
Responsibilities
- Work closely with clients to turn complex ideas into end-to-end data solutions
- Translate client requirements into system designs and develop solutions delivering measurable business value
- Lead, design, develop, and deliver large-scale data systems, data processing, data transformation, and data platform modernization initiatives
- Build and optimize batch and streaming data pipelines across modern cloud data platforms and distributed processing frameworks
- Design data foundations, retrieval patterns, context engineering approaches, and scalable data services supporting agentic and machine learning solutions
- Automate data platform operations and manage post-production systems, observability, quality, reliability, and operational processes
- Build telemetry pipelines capturing prompt, response, trace, latency, token, and cost data for AI-enabled services in queryable form
- Conduct technical feasibility assessments and provide project estimates
- Mentor, support, and grow junior team members while contributing hands-on to delivery
Requirements
- Demonstrable experience implementing end-to-end data pipelines and production-grade data platforms
- Hands-on experience with at least one leading public cloud data platform: Amazon Web Services, Microsoft Azure, or Google Cloud Platform
- Hands-on experience with Azure Cloud Services, including Azure Data Lake Storage (ADLS), Azure Functions, Azure Kubernetes Service (AKS), and Azure Databricks
- Experience with Databricks as a data engineering platform, including notebooks, jobs, Delta Lake, or similar lakehouse patterns
- Strong Python proficiency and practical experience using Python-based tooling for data engineering, automation, platform development, or AI engineering workflows
- In-depth knowledge of Scala, Apache Spark, PySpark, Python, Java, and shell scripting
- Experience with column-oriented, NoSQL, and traditional database technologies
- Experience implementing streaming and batch data pipelines using Glue ETL, Lambda, Google Cloud Dataflow, Azure Data Factory, Spark, Spark Streaming, or similar technologies
- Proficiency using Apache Airflow to orchestrate complex data workflows
- Experience with data modeling, warehouse design, fact/dimension implementations, and modern lakehouse or data mesh patterns
- Experience with code repositories, continuous integration, automated testing, release management, and production support practices
- Familiarity with MLOps concepts and AI/ML deployment, validation, monitoring, rollback, and operational reliability
- Ability to handle module or track-level responsibilities while contributing hands-on
- Good communication skills and willingness to work in a collaborative, cross-functional team
- Exposure to AI engineering patterns, context engineering, retrieval-augmented generation, agent architectures, and production data services
- Experience building pipelines for retrieval systems, including document parsing, chunking, metadata extraction, embedding generation, incremental reindexing, vector databases, graph databases, semantic search, and knowledge retrieval
- Exposure to agentic platforms or cloud AI services such as Vertex AI, Azure AI services, or AWS AI services
- Experience building evaluation data infrastructure for AI systems, including ground-truth and golden datasets, offline evaluation pipelines, LLM-as-judge, and regression testing
- Experience modeling and persisting agent state, session context, conversation history, and memory stores
- Developer certifications for AWS, Google Cloud, Microsoft Azure, Databricks, Snowflake, or related platforms are advantageous
- Experience applying AI engineering concepts in practical business environments
- Hands-on experience supporting AI/ML and LLM lifecycle needs
- Experience in retail, financial services, energy, CPG, logistics, manufacturing, or other data-rich industries
- Good understanding of capital markets domain requirements, particularly FINRA compliance
- Understanding of Agile, product, and delivery methodologies in consulting or client-facing environments
Benefits
- Ongoing learning and development opportunities
- Competitive compensation and benefits package
- Flexibility to support work-life balance
- Comprehensive health benefits for you and your family
- Generous paid leave and holidays
- Wellness program and employee assistance
- Inclusive workplace promoting diversity and collaboration
- Reasonable accommodations for qualified individuals with disabilities and disabled veterans
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