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    Job · 16906390

    BENGALURU · FULLTIME

    Engineer

    Eli Lilly and Company logo

    Eli Lilly and Company

    Bengaluru · onsite · Posted 41d ago

    Sourced fromNaukri
    Undisclosed2–7 yrsfulltimeBengaluru

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    Section · 01

    About this role

    Role
    The Senior Data Engineer - Data Experience Engineering is an experience-led senior individual contributor who starts from evidence about how scientists, statisticians, clinical data managers, safety, and regulatory teams actually discover, trust, and consume data - and then builds the pipelines and data products that answer those needs. The role works upstream of delivery, replacing assumption with structured user evidence, and carries that evidence through to production across Bronze / Silver / Gold Lakehouse pipelines on Databricks and AWS. It converts business and user needs into technology direction - capability requirements, evaluation criteria, and how enterprise platforms are selected and configured - and holds the engineering depth to prove those recommendations in working code. AI-assisted solutions are the expected way of working across both halves of the role, and as a senior engineer this role sets standards, mentors others, and packages proven patterns as reusable assets across Clinical and Non-Clinical squads.


    Key Responsibilities

    Experience-Led Discovery Data Product Definition
    • Plan and run mixed-methods research - contextual inquiry, interviews, usability testing, surveys, and behavioral analytics - with scientific, clinical, safety, regulatory, and business communities to determine which data products should exist and why.
    • Map user journeys and data workflows across Clinical and Non-Clinical domains, surfacing friction, workarounds, and unmet needs, and analyse product telemetry alongside qualitative findings so behaviour and stated need are read together.
    • Translate evidence into prioritised, decision-ready requirements, data contracts, and design principles, distinguishing genuine capability gaps from usability, adoption, and change-management gaps.


    Data Platform Pipeline Engineering
    • Independently design, build, and own end-to-end pipelines spanning Bronze / Silver / Gold Lakehouse layers on the Databricks + AWS ecosystem, owning reliability, performance, and cost for assigned data products.
    • Lead the design build of metadata-driven, reusable pipeline frameworks that reduce time-to-data, and set and enforce squad-level engineering standards and patterns.
    • Apply DataOps practices - automated testing, CI/CD, infrastructure-as-code (Terraform / Bicep), observability - and lead architecture and design reviews, surfacing risks and trade-offs early.


    AI Automation-Driven Engineering
    • Build self-healing, AI-augmented pipelines using anomaly detection and automated remediation to reduce manual intervention and improve reliability.
    • Apply LLM- and agent-based tooling to accelerate pipeline development, testing, and documentation, and to automate data quality checks, schema drift detection, and lineage capture.
    • Use AI-assisted methods as the default for research and synthesis - study design, transcript analysis, thematic synthesis, opportunity sizing - applying rigorous human judgment to guard against over-generalised conclusions.


    DaaS Delivery, Technology Direction Platform Evaluation
    • Deliver assigned domain data products from requirement definition through SLA-backed production operation, implementing data contracts and publishing documentation in the enterprise catalog for genuine self-service access.
    • Define and track experience measures (task success, time to insight, trust in data, adoption) and establish baselines that make improvement visible over time.
    • Shape how enterprise platforms and third-party solutions are evaluated, selected, and configured - user-centred criteria, proofs of concept, fit-gap analysis, and evidence-based build / buy / configure recommendations supported by working prototypes.
    • Advance Lillys federated data mesh model by applying domain data-ownership and governance patterns within assigned products.


    Technical Leadership Stakeholder Alignment
    • Mentor engineers through code review, pairing, and practical guidance, and establish the quality bar for how user evidence is gathered, interpreted, and reflected in build decisions.
    • Package proven engineering patterns, research methods, and accelerators as reusable components for adoption across Clinical and Non-Clinical squads.
    • Build effective relationships with clinical data managers, biostatisticians, product owners, and solution architects, communicating technical trade-offs, findings, and delivery status to technical and non-technical audiences alike.
    • Maintain alignment between business strategy and the technology landscape over time, supporting standards-compliant pipelines that accelerate availability of clinical trial data for regulatory submission.


    Qualifications Required

    Required - (Must-Have)
    • Bachelors degree in Computer Science, Data Engineering, or a related discipline.
    • Substantial hands-on data engineering, independently delivering production-grade pipelines and data platforms at senior level.
    • Direct exposure to Clinical and/or Non-Clinical data users and their workflows.


    Required - Engineering Depth (Must-Have)
    • Strong Python and SQL; solid working proficiency in PySpark / Spark for distributed data processing.
    • Cloud data platforms (Databricks AWS); data modelling across dimensional, normalised, and medallion / lakehouse patterns.
    • Pipeline orchestration (Databricks Workflows, Apache Airflow) and CI/CD DevSecOps practices: Git, Azure DevOps / GitHub Actions, automated testing, IaC.
    • Data observability and quality frameworks (Great Expectations, Monte Carlo, Acceldata, or equivalent).
    • Healthcare / clinical data standards awareness: CDISC, SDTM, ADaM, or OMOP.


    Required - Experience Evidence Technology Direction (Must-Have)
    • Proven, hands-on command of mixed-methods research applied to real enterprise or scientific user populations, including journey maps, personas, and opportunity frameworks for expert user groups.
    • Demonstrated experience converting business and user needs into technology direction - capability requirements, decision criteria, and roadmap input that engineering teams have adopted.
    • Hands-on experience evaluating, selecting, and configuring enterprise platforms or third-party solutions, including fit-gap analysis and build / buy / configure


    Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

    Sourced from naukri

    Section · 02

    Skills

    AutomationAzure DevopsCfradamclinical dataApacheautomated testingamazon web servicesapache airflowai automationBuilding AutomationAnalyticsCdiscAnomaly DetectionArchitecturebusiness strategyMicrosoft AzurebicepArtificial Intelligence

    Section · Company

    About Eli Lilly and Company

    Eli Lilly and Company logo

    Eli Lilly and Company

    Pharma

    2.7k+

    employees

    1993

    33 years old

    Indianapolis, Indiana

    United States (USA)

    ₹23.4L PA avg

    Avg at Eli Lilly and Company

    About

    Lilly unites caring with discovery to create medicines that make life better for people around the world.

    Industries

    Pharma

    Employee ratings

    10 reviews

    3.7/ 5

    Culture

    2.2

    Career growth

    2.0

    Work-life

    3.3

    Employees rate it well for

    Work-Life Balance39Job Security38

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