Lead consultant – Data Engineering (IMT60FT RM 3734)

Position: Lead consultant – Data Engineering (IMT60FT RM 3734)

Job Description:

• Architect, implement, and optimize data transformation pipelines, data lakes, and cloud-native warehouses for mid- and upper mid-market clients.
• Deliver hands-on engineering work across client environments — building fast, scalable, and well- documented pipelines that support both analytics and AI use cases.
• Lead technical design and execution using tools such as Tableau, Microsoft Fabric, Synapse, Power BI, Snowflake, and Databricks.
• Also have a good hands-on familiarity with SQL Databases.
• Optimize for sub-50GB datasets and local or lightweight cloud execution where appropriate — minimizing unnecessary reliance on cluster-based compute.
• Collaborate with subject-matter experts to understand business use cases prior to designing data model.
• Operate as a client-facing consultant: conduct discovery, define solutions, and lead agile project delivery.
• Switch context rapidly across 2–3 active clients or service streams in a single day.
• Provide support for our proprietary data products as needed.
• Provide advisory and strategic input to clients on data modernization, AI enablement, and FP&A transformation efforts.
• Deliver workshops, demos, and consultative training to business and technical stakeholders.
• Ability to implement coding modifications to pre-existing code/procedures in a manner that results in a validated case study (i.e. if done properly, the result will be “xyz” and the total amount will reconcile to “abc”).
• Take full ownership of hiring, onboarding, and mentoring future data engineers and analysts within the India practice.
• During bench time, contribute to building internal data products and tooling — powering our own consulting operations (e.g., utilization dashboards, delivery intelligence, practice forecasting).
• Help define and scale delivery methodology, best practices, and reusable internal accelerators for future engagements.
• Ability to communicate openly about conflicting deadlines to ensure prioritization aligns with client expectations, with ample time to reset client expectations as needed.
• Ensure coding is properly commented to help explain logic or purpose behind more complex sections of code.

Requirements:

• 6+ years of hands-on experience in data engineering roles, at least 3+ years in a consulting or client delivery environment.
• Proven ability to context-switch, self-prioritize, and communicate clearly under pressure.
• Demonstrated experience owning full lifecycle delivery, from architecture through implementation and client handoff.
• Strong experience designing and implementing ETL / ELT pipelines, preferably in SQL-first tools (e.g., dbt core, SQLMesh, DuckDB).
• Experience with Microsoft SQL server / SSIS for maintenance and development of ETL processes.
• Real-world experience with SQL Databases, Databricks, Snowflake, and/or Synapse — and a healthy skepticism of when to use them.
• Deep understanding of data warehousing, data lakes, data modeling, and incremental processing.
• Proficient in Python for ETL scripting, automation, and integration work.
• Experience with dbt core a comparable tool such as SQLMesh, Dataform, etc, in production environments.
• Strong practices around data testing, version control, documentation, and team-based dev workflows.
• Working knowledge of Power BI, Tableau, Looker, or similar BI tools — enough to support downstream teams but not as your primary skillset.
• Experience building platforms for AI/ML workflows or supporting agentic architectures.
• Familiarity with Microsoft Fabric’s Lakehouse implementation, Delta Lake, Iceberg, and Parquet.
• Background in DataOps, CI/CD for data pipelines, and metadata management.
• Microsoft certifications (e.g., Azure Data Engineer, Fabric Analytics Engineer) are a plus

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