← back to jobs
> job detail
F
👽Other

Data Scientist

Ford Motor · Chennai, Tamil Nadu, India
// classified as
Other (Adjacent or hard to classify.)
posted
<1d ago
location
Chennai, Tamil Nadu, India
languages
python, sql
tools
aws, azure, bigquery
> stack
pythonsqlawsazurebigquerydbthadoopsparkdbt
> education
masters
> description

About Us: 

In Global Data Insight & Analytics (GDI&A), we harness the power of data and artificial intelligence to navigate Ford Motor Company through the disruptiveness of the information age. We're a team of innovators who strive to realize the enterprise's goals, reveal hidden opportunities, and achieve data superiority. 

About You: 

We are looking for a hands-on Data Engineer with 3+ years of experience building production-grade data pipelines, cloud data platforms, and automated data workflows. You are comfortable working across structured, semi-structured, and unstructured data; you understand the importance of data quality, lineage, security, and cost optimization; and you are excited to build the data foundation required for modern AI, ML, and GenAI use cases. 

  • Understand business, analytics, and AI use cases and translate them into scalable data engineering solutions. 

  • Design, build, and maintain reliable batch and streaming data pipelines for ingestion, transformation, validation, and publishing. 

  • Develop curated, reusable, and well-documented data products that support BI dashboards, analytics applications, ML models, and GenAI-enabled solutions. 

  • Implement strong data quality checks, observability, lineage, metadata management, and monitoring practices to improve trust in enterprise data assets. 

  • Write clean, modular, and well-tested code using Python, SQL, and modern data engineering frameworks. 

  • Use cloud-native technologies such as BigQuery, Dataflow, Dataproc, Cloud Composer/Airflow, Dataform, DBT, Spark, or equivalent tools to deliver resilient data solutions. 

  • Enable AI/ML and GenAI teams by preparing high-quality feature datasets, vector-ready datasets, document corpora, and governed data access patterns. 

  • Partner with data scientists, ML engineers, product owners, and business stakeholders to support experimentation, model deployment, and production analytics. 

  • Apply DataOps practices including CI/CD, version control, automated testing, reusable templates, release management, and production support standards. 

  • Optimize pipeline performance, storage usage, compute cost, and reliability across cloud-based data platforms. 

  • Support data governance, privacy, access control, and compliance expectations for enterprise and AI-ready data assets. 

  • Stay current with advances in cloud data engineering, AI data infrastructure, orchestration, data quality, and GenAI-enabling technologies. 

  • Minimum Qualifications: 

  • Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Systems, Engineering, Statistics, Mathematics, or related technical field. 

  • 3+ years of hands-on experience in data engineering, ETL/ELT development, data warehousing, or cloud-based data platform delivery. 

  • Strong proficiency in SQL and Python for data extraction, transformation, automation, testing, and production support. 

  • Experience designing and operating scalable pipelines on cloud platforms such as Google Cloud Platform, AWS, Azure, or equivalent enterprise data ecosystems. 

  • Experience with modern data platforms and tools such as BigQuery, Spark, Dataflow, Dataproc, Airflow/Cloud Composer, Dataform, DBT, or similar technologies. 

  • Good understanding of data modeling, dimensional modeling, partitioning, clustering, performance tuning, and cost optimization. 

  • Working knowledge of data quality frameworks, monitoring, alerting, metadata, lineage, and production support practices. 

  • Familiarity with Git, CI/CD, agile delivery, code reviews, documentation, and reusable engineering standards. 

  • Strong communication skills with the ability to explain technical solutions clearly to engineering, analytics, and business stakeholders. 

  • Preferred Qualifications: 

  • 5+ years of experience delivering enterprise data engineering solutions in cloud-native environments. 

  • Experience building data products for AI/ML, GenAI, semantic search, retrieval-augmented generation, feature engineering, or model monitoring use cases. 

  • Experience working with unstructured data such as documents, logs, text, images, transcripts, or embeddings, and preparing them for downstream AI consumption. 

  • Hands-on experience with DataOps, MLOps enablement, pipeline observability, automated testing, and production incident resolution. 

  • Experience migrating legacy workflows from Hadoop, Alteryx, or on-premise platforms to modern cloud services. 

  • Experience with APIs, microservices, event-driven architectures, streaming data, or real-time analytics. 

  • Cloud certifications in Google Cloud Platform, AWS, Azure, or relevant data engineering technologies. 

  • Experience mentoring junior engineers, defining engineering standards, or contributing reusable platform accelerators. 

Apply at Ford Motor →