Website:
casamenterorecruit.in
Job details:
Job Title: Data/Analytics Engineer
Experience: 3–6 years
Client: Bajaj Capital
Location: Gurgaon (WFO 6 DAYS)
Compensation Package: Competitive - as per market standards.
Department: Data Science
Role Overview
We are looking for a skilled and hands-on Data/Analytics Engineer to build and maintain the backbone of our data infrastructure. In this role, you will be responsible for setting up robust data flows for AI and Data Analytics, ensuring that high-quality data is available for modelling and decision-making. You will bridge the gap between data engineering and data science by implementing efficient ETL pipelines and establishing strong ML Ops practices.
If you are proficient in Python, Airflow, and the Google Cloud ecosystem, and thrive on optimizing data architecture for scale.
Key Responsibilities
Data Pipeline & ETL Architecture
· Pipeline Orchestration: Design, develop, and maintain scalable ETL/ELT pipelines using Apache Airflow and Python to automate data workflows.
· Data Integration: seamless integration of data from various RDBMS sources and external APIs into Google BigQuery to create a unified data source for analytics and AI.
· Data Flow Optimization: Ensure efficient data flow for AI and analytics use cases, optimizing for latency, cost, and reliability.
ML Ops & Model Deployment
· Model Operationalization: collaborate with Data Scientists to deploy machine learning models into production using Google Cloud Vertex AI.
· ML Ops Implementation: Establish and maintain ML Ops practices, including model monitoring, versioning, retraining pipelines, and CI/CD for machine learning.
· Infrastructure Management: Manage the underlying infrastructure required for model training and serving, ensuring high availability and performance.
Data Warehousing & Governance
· BigQuery Management: Manage and optimize data warehousing in Google BigQuery, ensuring appropriate schema design, partitioning, and clustering for performance.
· Data Quality: Implement checks and balances to ensure data accuracy and consistency across the pipeline.
· Documentation: Maintain comprehensive documentation of data lineage, pipeline architecture, and operational runbooks.
Collaboration
· Work as key member of the Data Science team to understand model requirements and provide the necessary data infrastructure.
· Collaborate with the Tech and Business teams to integrate data pipelines with broader organizational infrastructure.
Must-Have Skills & Experience
Experience:
· 3–6 years of professional experience in Data Engineering, Analytics Engineering, or ML Engineering.
· Proven experience in building production-grade data pipelines and deploying ML models.
Technical Proficiency:
· Core Languages: Strong proficiency in Python for data manipulation and scripting.
· Orchestration: Expert-level knowledge of Apache Airflow for scheduling and monitoring workflows.
· Cloud Data Stack: Deep expertise in Google Cloud Platform (GCP), specifically BigQuery for warehousing and Vertex AI for AI/ML workloads.
· Database & ETL: Strong command over RDBMS (SQL) and extensive experience with ETL operations to transform raw data into analytical assets.
· ML Ops: Familiarity with ML Ops tools and methodologies (e.g., Kubeflow, MLflow, or native Vertex AI pipelines).
Soft Skills:
· Strong problem-solving skills and attention to detail.
· Ability to work in a fast-paced, agile environment.
· Good communication skills to articulate technical challenges to non-technical stakeholders.
Good-to-Have / Plus
· Domain Expertise: Prior experience in the BFSI domain (Wealth Management, Insurance, Mutual Funds).
· Certifications: Google Professional Data Engineer or Google Professional Machine Learning Engineer certifications. Exposure to AWS S3 and RDS.
Education
· B.Tech / B.E. / MCA / M.Sc or equivalent in Computer Science, Information Technology, or Data Science.
"Relevant profiles are encouraged to apply with their updated CV. You can also reach out to me PZ.Manas@casamenterorecruit.in OR WhatsApp: +91-8431783018."
Click on Apply to know more.