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Data Engineer with 5+ years of experience: We are seeking a Data Engineer with strong dual competency in data and life science to build and scale data pipelines and curated datasets that power scientific analytics, AI/ML, and data products across R&D.; You will work in a multidisciplinary environment, partnering closely with scientists, data scientists, data architects, and product owners to translate scientific workflows into reliable, reusable, and governed data assets. This role is hands-on and delivery-focused: you will design, develop, and operate data ingestion and transformation pipelines, optimize performance and reliability, and ensure data is discoverable and trusted through strong metadata, lineage, and access controlswithin the scientific data and AI ecosystem for development and RWE assets. Key Responsibilities: Build & Operate Data Pipelines: Design and implement scalable ingestion and transformation pipelines for diverse scientific datasets (structured and unstructured), using Databricks as a core execution environment. Develop robust ETL/ELT workflows, including batch and (where relevant) incremental processing, with production-grade practices (testing, monitoring, reliability). Collaborate with platform and security teams to ensure pipelines follow access, governance, and operational standards. Build and maintain data structures and curated layers to support analytics, reporting, and downstream data products. Optimize performance and cost through good engineering practices (e.g., efficient transformations, workload tuning, data lifecycle considerations). Scientific Context & Business: Work effectively with researchers and scientists to understand workflows and ensure pipelines preserve scientific meaning, context, and traceability. Support analytical environments (e.g., Power BI, Tableau) by delivering data in formats and structures fit for scientific exploration and enterprise connectivity. Data Quality, Metadata, Lineage & Governance: Embed data quality checks and validation steps into pipelines to ensure data is reliable and fit for scientific decision-making, and follow data strategy. Implement and maintain metadata and lineage practices so that datasets are discoverable and governed (e.g., cataloging, traceability, access controls). Contribute to governance-by-design by working with architects and governance leads on standards and reusable patterns. Collaboration & Delivery: Partner with Data Architects and product teams to implement agreed data models and patterns in production pipelines. Support sprint delivery by estimating, implementing, and continuously improving pipelines and datasets based on feedback and evolving use cases. Governance, Quality and Alignment: Ensure products comply with data governance, quality, privacy, and compliance requirements in collaboration with Data Governance and Architecture teams. Align product decisions with architectural standards and platform capabilities, balancing short-term needs with long-term sustainability. Promote reuse and consistency across R&D; data products. Qualifications & Experience: Bachelors or Masters degree (or equivalent experience) in Computer Science, Data Engineering, Bioinformatics, Biomedical Sciences, Life Sciences, or a related field. 5+ years in data engineering / data platform engineering (or equivalent). Strong hands-on experience with operating data pipelines in a production workplace. Proven ability to work across scientific and technical domains collaborating credibly with scientists and technical teams. Experience working with clinical and RWE datasets and workflows. Experience with metadata/catalog and lineage practices in enterprise environments. Required Skills & Competencies: Data pipeline engineering, transformation design, performance optimization. Expertise with ETL/ELT, collaborative engineering workflows like Snowflake or Databricks. Experience with data modelling, data curation. Experience with data visualization and analytics. Strong SQL + Python (and comfort supporting scientific tooling needs such as R where relevant). Expertise in FAIR data principles, meta data, data catalogues. Previous experience with semantic, ontology and knowledge graph is a plus. Experience working in regulated environments (e.g. GxP, data privacy) is a plus. .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.