AI Data Engineer

Boston's interview process typically starts with a timed online assessment focusing on SQL, Python, and logical reasoning, followed by a technical interview that dives deep into data pipeline design, cloud services, and problem‑solving. The final HR round assesses cultural fit and communication skills. Candidates should be ready to discuss past projects, explain design choices, and demonstrate a strong grasp of big‑data tools.

Boston promotes a results‑oriented work culture where collaboration and continuous learning are highly valued. Employees enjoy flexible remote arrangements, regular knowledge‑sharing sessions, and clear career progression pathways. The firm emphasizes work‑life balance through generous leave policies and wellness initiatives.

Graduates (B.Tech/B.E., M.Tech, B.Sc.) in Computer Science, Information Technology, Electronics & Communication, or related streams; Minimum 60% aggregate (or CGPA 6.5/10); Batch 2025‑2026; No active backlogs; Strong programming fundamentals; Passion for data engineering and cloud technologies.

Round 1: Online Assessment → Round 2: Technical Interview → Round 3: HR Interview

Boston is a global consulting and technology firm that blends deep industry expertise with cutting‑edge data science to help clients transform their businesses. With a presence in over 30 countries, Boston focuses on delivering end‑to‑end solutions that span strategy, analytics, and implementation. The company prides itself on a collaborative culture where innovation is encouraged, and employees are given the freedom to experiment with emerging technologies such as AI, cloud computing, and big data platforms. Over the past few years, Boston has invested heavily in building a robust data engineering practice, enabling clients to unlock value from massive, heterogeneous data sources. The AI Data Engineer role is a cornerstone of Boston’s data‑driven initiatives. As an AI Data Engineer, you will be responsible for designing, building, and maintaining scalable data pipelines that ingest, cleanse, transform, and store data across cloud data lakes, warehouses, and analytical platforms. You will work closely with case teams, data scientists, and product owners to ensure that data is reliable, secure, and readily available for downstream analytics and machine‑learning models. The role offers exposure to a variety of industries, from finance to healthcare, allowing you to apply your technical skills to solve real‑world business problems. Key responsibilities include: 1. Develop end‑to‑end ETL/ELT pipelines using Python, PySpark, and SQL. 2. Optimize data workflows for performance, cost, and reliability on cloud platforms (AWS/GCP/Azure). 3. Implement data quality checks, monitoring, and alerting mechanisms. 4. Collaborate with data scientists to provision feature stores and model training datasets. 5. Manage infrastructure as code using Terraform or CloudFormation. 6. Automate deployments with Airflow, Jenkins, or GitHub Actions. 7. Containerize services with Docker and orchestrate using Kubernetes. 8. Ensure compliance with security standards and data governance policies. 9. Document pipeline architecture, data lineage, and best practices. 10. Participate in code reviews and mentor junior engineers. The tech stack revolves around PySpark, Hive, Snowflake, Databricks, and major cloud services, complemented by DevOps tools like Docker, Kubernetes, Terraform, and CI/CD pipelines. Boston offers a clear growth path: junior engineers can progress to senior engineer, lead data architect, or product manager roles, with opportunities to specialize in AI/ML engineering or cloud architecture. Joining Boston means working in a high‑impact environment, gaining mentorship from industry veterans, and contributing to projects that shape the future of data‑driven decision making. The company’s emphasis on continuous learning, flexible remote work, and competitive compensation makes it an attractive destination for ambitious fresh graduates.