Data Engineering - Consultant
FormativGroup
| Company | FormativGroup |
| Category | Uncategorised |
| Location | Remote/India - Full-Time |
| Remote | Remote |
| Employment | Not stated |
| Level | Not stated |
| Salary | Not stated by the employer |
| Posted | 30 Jul 2026 |
| Last verified | 30 Jul 2026 |
| Source | Employer career page (greenhouse) |
Description
At our core, we help organizations unlock the full value of their data - turning complexity into clarity and strategy into measurable outcomes. As a Consultant - Data Engineering & Analytics - Data Engineering, you will support the delivery of data solutions and contribute to client transformation initiatives.
This role focuses on building on foundational experience at the intersection of data engineering, analytics, and consulting delivery. You will work alongside experienced team members to help design, develop, and implement modern, cloud-based data solutions that enable efficient analytics and data-driven decision-making.
You will support development efforts across platforms such as Snowflake, Databricks, AWS, and Azure, contributing to client data modernization initiatives. You will assist in translating business and technical requirements into well-structured data solutions while developing your technical and consulting skill set.
Success in this role requires strong problem-solving abilities, attention to detail, and a willingness to learn in a fast-paced environment. You will be expected to contribute to team deliverables, continuously build technical expertise, and support high-quality client outcomes.
What You'll Work On
Support client engagements as a contributing project team member
Assist in gathering and understanding business and technical requirements
Translate requirements into technical tasks under guidance from senior team members
Support preparation of presentations, documentation, and deliverables
Develop and support data solutions leveraging:
Snowflake (data warehousing, data storage)
Databricks (data processing, Spark workloads)
AWS ecosystem (S3, Glue, Redshift, Lambda, EMR, IAM)
Microsoft Fabric / Azure (Synapse, ADF)
DataVault Data Automation
Build and maintain data pipelines (primarily batch, with exposure to near real-time)
ETL/ELT development and data integration processes
Contribute to development of data platforms including data lakes and data warehouses
Follow established best practices for coding, testing, and performance optimization
Support senior team members in troubleshooting and resolving data issues
Participate in code reviews, testing, and quality assurance activities
Manage assigned tasks and deliver them within project timelines with minimal oversight
Assist with analytics and reporting by preparing curated datasets
Support advanced analytics initiatives such as machine learning data preparation
Stay current with tools, technologies, and best practices through continuous learning
Support data governance, data quality, and documentation efforts
Follow data security and compliance standards
Contribute to maintaining metadata, lineage, and technical documentation
What You'll Bring
Bachelor’s degree in Data Analytics, Business Analytics, Information Systems, Computer Science, Statistics, Mathematics, Economics, or related field
3-5 years of experience in data engineering, analytics, or related technical roles
Foundational experience with:
SQL and basic data modeling concepts
ETL/ELT processes and data integration
Exposure to cloud platforms such as AWS, Azure, or GCP
Familiarity with tools such as Snowflake, Databricks, or similar platforms is a plus
Exposure to programming languages such as Python or Scala is preferred
Basic understanding of modern data architecture concepts (data lakes, warehouses)
Exposure to data visualization tools (e.g., Power BI, Tableau) is a plus
Strong analytical and problem-solving skills
How You Work
Need to be proactive and look for ways to pitch in
Someone who is customer oriented and quality driven
Much be able to handle multiple priorities and have good time management
This is an ever-changing landscape so the ability to be flexible with routines is important
Thinking outside the box to find better ways of worki