Senior Big Data Engineer - Chicago Hybrid

We need to output HTML fragment with sections: headings

for sections like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Use

• for any list of duties/requirements/benefits, and

  for prose. Do NOT repeat job title as heading. Do NOT put Job ID/requisition numbers as headings. Preserve all substantive info. Remove boilerplate (cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, application instructions). Include a section ONLY when posting has real, specific content for it — never emit heading with filler. Prefer few substantive sections over many thin ones. We need to parse the job description. Key parts: - Title: Senior Associate Data Engineering - Chicago Hybrid (but we don't repeat as heading) - About the role: The first paragraph after title: "As a Senior Associate Data Engineer, you will be responsible for designing, developing, and maintaining scalable Big Data solutions. You will work with large datasets, real-time processing frameworks, and cloud-based data platforms to enable data-driven decision-making for our clients." - Then "Your Impact:" bullet list (multiple sentences). This could be responsibilities. - Then "Qualifications" heading: includes "Your Skills & Experience:" bullet list. - Then "AI Engineering & Modern Data Platform Experience:" bullet list. - Then "Set Yourself Apart With:" bullet list. - Then "Additional Information" includes Salary Range and Benefits. - Benefits: bullet list after "Benefits Of Working Here". - Also "Company Description" maybe not needed? It's boilerplate? Might be considered substantive but maybe not required. The instruction: preserve ALL substantive information; do NOT invent or summarize away detail. Remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions. Company description may be considered substantive but not a standard heading; we could include as "About the company" but not in list of allowed headings? The allowed headings list includes About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We could put company description under About the role or maybe as a separate section not listed? Probably better to incorporate into About the role or maybe omit if not needed. But we must preserve all substantive info; if we omit company description, we lose info. However maybe it's considered boilerplate? It's describing the company; could be considered substantive but not required to be separate section; we could embed in About the role as a paragraph. We need to decide sections. We have: - About the role: first paragraph. - Responsibilities: could be the "Your Impact:" bullet points. - Requirements/Qualifications/Skills: we have multiple bullet lists: "Your Skills & Experience:", "AI Engineering & Modern Data Platform Experience:", "Set Yourself Apart With:". These could be combined under Qualifications or Skills. Since we prefer few substantive sections, we could combine all qualification-like bullet lists under a single section "Qualifications" (or "Requirements") with sub-lists? But we must use

  • for any list. We could have multiple
    inside the section. We also have Pay: Salary Range. Benefits: bullet list. Schedule: not mentioned (Hybrid mentioned in title but not as schedule). Could we infer schedule? The title says Chicago Hybrid, but schedule not explicitly given. We should not create Schedule section unless posting actually states them. It does not state schedule explicitly, so we should not include Schedule. Thus sections: About the role (p), Responsibilities (ul), Qualifications (ul maybe with multiple subheadings? but we cannot use headings inside? We could just have one big list combining all bullet points. However we need to preserve the grouping? Might be okay to just list all qualification bullet points as a single list. But we lose the categorization (Skills & Experience, AI Engineering..., Set Yourself Apart With). However we could keep headings inside the section as

    bold? Not allowed? Only allowed headings are

    for section headings. We could use

    for prose; we could include a

    with a label like "Skills & Experience:" then a

    . That's acceptable: we can have paragraphs introducing subsections. Thus Qualifications section:

    Qualifications

    then

    Skills & Experience:

    ... then

    AI Engineering & Modern Data Platform Experience:

    ... then

    Set Yourself Apart With:

    ... Similarly Benefits:

    Benefits

    then
    . Pay:

    Pay

    then

    Salary Range: $136,000 - $160,000 annual salary

    maybe also note about range variation. We should also include location/hybrid? Not required as heading; could be included in About the role paragraph. We must remove boilerplate: EEO statement, accommodations, contact email, company description? The company description may be considered substantive but not required heading; we could incorporate as a paragraph under About the role after the first paragraph? Might be okay. But the instruction: "Remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions." The EEO statement is boilerplate; remove it. The company description is not EEO; it's about the company; could be kept. We also need to remove duplicated lines: there may be duplicated sentences like "Support AI-enabled engineering use cases by designing high-quality data foundations..." appears twice? Actually appears in Your Impact and also later in AI Engineering section? Let's check: In Your Impact: "Support AI-enabled engineering use cases by designing high-quality data foundations, retrieval patterns, context engineering approaches, and scalable data services that power agentic and machine learning solutions." Then later in AI Engineering & Modern Data Platform Experience: there is a similar line: "Support AI-enabled engineering use cases by designing high-quality data foundations, retrieval patterns, context engineering approaches, and scalable data services that power agentic and machine learning solutions, applying the same lineage, provenance, and data contract rigor to context and retrieval sources that you would to a production warehouse." Slightly different but similar. We should keep both as they are not exact duplicates; but we can keep both. We must not invent or summarize away detail. Now produce HTML fragment. Structure:

    About the role

    As a Senior Associate Data Engineer, you will be responsible for designing, developing, and maintaining scalable Big Data solutions. You will work with large datasets, real-time processing frameworks, and cloud-based data platforms to enable data-driven decision-making for our clients. Publicis Sapient is a digital transformation partner helping established organizations get to their future, digitally enabled state, both in the way they work and the way they serve their customers. We help unlock value through a start-up mindset and modern methods, fusing strategy, consulting, and customer experience with agile engineering and problem-solving creativity. United by our core values and our purpose of helping people thrive in the brave pursuit of next, our 20,000+ people in 53 offices around the world combine experience across technology, data sciences, consulting, and customer obsession to accelerate our clients’ businesses through designing the products and services their customers truly value.

    Responsibilities

    • Combine your technical expertise and problem-solving passion to work closely with clients, turning complex ideas into end-to-end data solutions that transform our clients’ business.
    • Translate client requirements into system design and develop solutions that deliver 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.
    • Support AI-enabled engineering use cases by designing high-quality data foundations, retrieval patterns, context engineering approaches, and scalable data services that power agentic and machine learning solutions.
    • Automate data platform operations and manage post-production systems, observability, quality, reliability, and operational processes, including telemetry pipelines that capture prompt, response, trace, latency, token, and cost data for AI-enabled services in a query able form.
    • Conduct technical feasibility assessments and provide project estimates for the design and development of solutions.
    • Mentor, support, and grow junior team members while contributing hands-on to delivery.

    Qualifications

    Skills & Experience:

    • 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.
    • Experience with Databricks as a data engineering platform is strongly preferred, including working with 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.
    • Implementation experience with column-oriented database technologies such as BigQuery, Redshift, Vertica, or similar platforms; NoSQL database technologies such as DynamoDB, Bigtable, Cosmos DB, or similar; and traditional database systems such as SQL Server, Oracle, or MySQL.
    • Experience implementing data pipelines for both streaming and batch integrations using tools and frameworks such as Glue ETL, Lambda, Google Cloud Dataflow, Azure Data Factory, Spark, Spark Streaming, or similar technologies.
    • 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 the data engineering responsibilities required to support AI/ML deployment, validation, monitoring, rollback, and operational reliability.
    • Ability to handle module or track-level responsibilities while contributing to tasks hands-on.
    • Good communication skills and willingness to work as part of a collaborative, cross-functional team.

    AI Engineering & Modern Data Platform Experience:

    • Exposure to AI engineering patterns, including context engineering, retrieval-augmented generation support patterns, agent architectures, and production data services that support AI-enabled experiences.
    • Experience building and maintaining the pipelines behind retrieval systems, including document parsing, chunking, metadata extraction, embedding generation, and incremental reindexing, alongside the vector databases, graph databases, semantic search, and knowledge retrieval structures they feed.
    • Exposure to agentic platforms or cloud AI services such as Vertex AI, Azure AI services, AWS AI services, or comparable platforms; specific platform experience is less important than understanding how AI engineering differs from traditional data engineering.
    • Practical experience deploying agents, integrating agent frameworks, or supporting agentic workflows in production or near-production environments is a plus.
    • Experience building evaluation data infrastructure for AI systems, including ground-truth and golden datasets, offline evaluation pipelines, and the data scaffolding behind LLM-as-judge and regression testing.
    • Experience modeling and persisting agent state, including session context, conversation history, and memory stores, treating them as a durable storage and data modeling problem rather than an application detail.
    • Support AI-enabled engineering use cases by designing high-quality data foundations, retrieval patterns, context engineering approaches, and scalable data services that power agentic and machine learning solutions, applying the same lineage, provenance, and data contract rigor to context and retrieval sources that you would to a production warehouse.
    • Experience with agentic harnesses or orchestration tools such as Pi, Hermes Agent, or similar platforms is a plus, but not required.
    • Experience with Snowflake and zero-copy architecture patterns is a plus, particularly for retail, financial services, energy, or CPG-oriented use cases.

    Set Yourself Apart With:

    • Developer certifications for