Freight Broker (Salaried Position)

SENIOR DATA ENGINEER:

ROLE SUMMARY:

Own the design and delivery of petabyte-scale data platforms and pipelines across AWS and modern Lakehouse stacks. You’ll architect, code, test, optimize, and operate ingestion, transformation, storage, and serving layers. This role requires autonomy, strong engineering judgment, and partnership with project managers, infrastructure teams, testers, and customer architects to land secure, cost-efficient, and high-performing solutions.

RESPONSIBILITIES:

• Architecture and design: Create HLD/LLD/SAD, source–target mappings, data contracts, and optimal designs aligned to requirements.
• Pipeline development: Build and test robust ETL/ELT for batch, micro-batch, and streaming across RDBMS, flat files, APIs, and event sources.
• Performance and cost tuning: Profile and optimize jobs, right-size infrastructure, and model license/compute/storage costs.
• Data modeling and storage: Design schemas and SCD strategies; manage relational, NoSQL, data lakes, Delta Lakes, and Lakehouse tables.
• DevOps and release: Establish coding standards, templates, CI/CD, configuration management, and monitored release processes.
• Quality and reliability: Define DQ rules and lineage; implement SLA tracking, failure detection, RCA, and proactive defect mitigation.
• Security and governance: Enforce IAM best practices, retention, audit/compliance; implement PII detection and masking.
• Orchestration: Schedule and govern pipelines with Airflow and serverless event-driven patterns.
• Stakeholder collaboration: Clarify requirements, present design options, conduct demos, and finalize architectures with customer teams.
• Leadership: Mentor engineers, set FAST goals, drive upskilling and certifications, and support module delivery and sprint planning.

REQUIRED QUALIFICATIONS:

• Experience: 15+ years designing distributed systems at petabyte scale; 10+ years building data lakes and multi-source ingestion.
•  Cloud (AWS): IAM, VPC, EC2, EKS/ECS, S3, RDS, DMS, Lambda, CloudWatch, CloudFormation, CloudTrail.
• Programming: Python (preferred), PySpark, SQL for analytics, window functions, and performance tuning.
• ETL tools: AWS Glue, Informatica, Databricks, GCP DataProc; orchestration with Airflow.
• Lakehouse/warehousing: Snowflake, BigQuery, Delta Lake/Lakehouse; schema design, partitioning, clustering, performance optimization.
• DevOps/IaC: Terraform with 15+ years of practice; CI/CD (GitHub Actions, Jenkins) with 10+ years; config governance and release management.
• Serverless and events: Design event-driven distributed systems on AWS.
• NoSQL: 2–3 years with DocumentDB including data modeling and performance considerations.
• AI services: AWS Entity Resolution, AWS Comprehend; run custom LLMs on Amazon SageMaker; use LLMs for PII classification.

NICE-TO-HAVE QUALIFICATIONS:

• Data governance automation: 10+ years defining audit, compliance, retention standards and automating governance workflows.
• Table and file formats: Apache Parquet; Apache Iceberg as analytical table format.
• Advanced LLM workflows: RAG and agentic patterns over proprietary data; re-ranking with index/vector store results.
• Multi-cloud exposure: Azure ADF/ADLS, GCP Dataflow/DataProc; FinOps practices for cross-cloud cost control.

OUTCOMES AND MEASURES:

• Engineering excellence: Adherence to processes, standards, and SLAs; reduced defects and non-compliance; fewer recurring issues.
• Efficiency: Faster run times and lower resource consumption with documented cost models and performance baselines.
• Operational reliability: Faster detection, response, and resolution of failures; quick turnaround on production bugs; strong release success.
• Data quality and security: High DQ pass rates, robust lineage, minimal security incidents, and audit readiness.
• Team and customer impact: On-time milestones, clear communication, effective demos, improved satisfaction, and completed certifications/training.

LOCATION AND SCHEDULE:

●      Location: Outside US (OUS).

●      Schedule: Minimum 6 hours of overlap with US time zones.

 Digital Marketing Internship

• Must have good insight into tools like Google Analytics, SEO tools, social media management platforms, and more.
• Design posters and creatives for Instagram, websites, and other platforms
• Assist in creating and executing digital marketing campaigns
• Enthusiasm for digital marketing and a willingness to learn.
• Strong written and verbal communication skills.
• Basic understanding of social media platforms and trends.
• Familiarity with tools like Canva, Google Docs, or any analytics tool.
• Work on content creation, SEO, and social media strategies
• Knowledge of social media trends and strategies
• Self-motivated, creative, and eager to learn

Mode - Remote

We are looking for someone with good communication skills and okay with sales and operations. As of now, this will be 70% sales and 30% operations.

Key responsibilities:

1. Promoting and selling memberships to potential new clients (walk-ins/trials)

2. Working on achieving the target by driving conversions

3. Working on upselling

4. Maintaining a record of every interaction with new users

5. Working on entering all the details of customers and walk-ins in internal tools

6. Managing the center experience and incremental enhancements

7. Handling the timely opening and closing of the center

8. Working on inventory management

Note:

1. Role name: Associate Center Manager

2. CTC: 3 LPA fixed

3. Number of working days: 6

4. Week off: 1 (rotational)

5. Shift timings: split shift (5:30 am to 10 am, break, 5 pm to 10 pm)

1. Create Exclusive Distributors (minimum 4 in 4 months) in your respective areas of operation

1. To work directly with the distributor and the sales executives in designing and implementing key marketing campaigns.

1. To actively engage yourself to collect inventory, stocks and work reports of the distributor in a

daily/weekly basis.

1. To look after smooth functioning and supply chain of the authorized distributor under your

periphery.

1. To oversee sales executive schedule and plan them accordingly.

1. To facilitate cross-functional communications among project stakeholders.

1. To actively engage yourself with core marketing policies required for business development.

1. To look after the grievances and claims arising from time to time maintaining proper

coordination with the company.

1. To actively follow other marketing strategies and instructions as and when instructed by the company.

• Required to work individually or as part of a team on data science projects and work closely with lines of business to understand business problems and translate them into identifiable machine learning problems which can be delivered as technical solutions.
• Build quick prototypes to check feasibility and value to the business.
• Design, training, and deploying neural networks for computer vision and machine learning-related problems.
• Perform various complex activities related to statistical/machine learning.
• Coordinate with business teams to provide analytical support for developing, evaluating, implementing, monitoring, and executing models.
• Collaborate with technology teams to deploy the models to production.

Key Criteria:

• 2+ years of experience in solving complex business problems using machine learning.
• Understanding and modeling experience in supervised, unsupervised, and deep learning models; hands-on knowledge of data wrangling, data cleaning/ preparation, dimensionality reduction is required.
• Experience in Computer Vision/Image Processing/Pattern Recognition, Machine Learning, Deep Learning, or Artificial Intelligence.
• Understanding of Deep Learning Architectures like InceptionNet, VGGNet, FaceNet, YOLO, SSD, RCNN, MASK Rcnn, ResNet.
• Experience with one or more deep learning frameworks e.g., TensorFlow, PyTorch.
• Knowledge of vector algebra, statistical and probabilistic modeling is desirable.
• Proficiency in programming skills involving Python, C/C++, and Python Data Science Stack (NumPy, SciPy, Pandas, Scikit-learn, Jupyter, IPython).
• Experience working with Amazon SageMaker or Azure ML Studio for deployments is a plus.
• Experience in data visualization software such as Tableau, ELK, etc is a plus.
• Strong analytical, critical thinking, and problem-solving skills.

• B.E/ B.Tech./ M. E/ M. Tech in Computer Science, Applied Mathematics, Statistics, Data Science, or related Engineering field.
• Minimum 60% in Graduation or Post-Graduation
• Great interpersonal and communication skills