Data engineering and analytics

Ingest. Integrate. Drive better decisions.

Ingest. Integrate. Drive better decisions.

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The problem

Your data foundation is holding your business back.

Legacy platforms are too slow for real-time decisions. Data is trapped in silos, ETL pipelines are brittle, and rising maintenance costs are stalling your AI initiatives.

Meanwhile, manual compliance and unclear data lineage turn your infrastructure into a liability. You aren't just managing data, you're managing a growing bottleneck to your growth.

You do not need more dashboards. You need an engineering partner who can unify your data, automate your pipelines, and build a scalable foundation.

30+

Years average individual experience per engineer

90%

Improvement in data accuracy and accessibility

Databricks

Databricks

consulting partner

Solution overview

Data engineering built for scale, speed, and compliance

Tenjumps builds modern data infrastructure from the foundation up. We design architectures, engineer pipelines, and implement the governance frameworks required to drive real business decisions. Every engagement runs on our AI-driven delivery model with built-in security, compliance, and monitoring from day one. We don't just move data; we build the reliable systems that power your intelligence.

01

Data architecture and models

Scalable foundations for a unified truth

  • Modern data platforms: We design and implement relational databases, NoSQL stores, data lakes, and cloud-native warehouses across AWS, Azure, and GCP.

  • Databricks expertise: As a consulting partner, we build lakehouse architectures using Delta Lake for ACID-compliant storage and Unity Catalog for centralized governance.

  • Medallion architecture: We organize data into bronze, silver, and gold layers to move from raw ingestion to business-ready assets.

  • Vendor-agnostic strategy: We ensure your architecture serves your specific business goals, avoiding restrictive vendor lock-in.

02

Data quality and governance

Built-in trust and regulatory readiness

  • Native quality controls: We embed schema validation, referential integrity checks, and automated duplicate detection from the start.

  • Automated classification: Our systems automatically identify and tag PII, applying field-level masking and tokenization where required.

  • Comprehensive compliance: We support global standards through attribute-based access controls, full data lineage, and encryption at rest and in transit.

  • Unified control plane: We provide a single point of truth for audit trails, ensuring your data is a secure asset rather than a liability.

03

Pipelines and integration

High-velocity engineering for real-time flow

  • Modular engineering: We build scalable pipelines for batch, real-time streaming, and micro-batch ingestion from APIs, webhooks, and CDC.

  • Automated orchestration: Using Delta Live Tables and Databricks Workflows, we manage complex dependencies, retries, and failure alerting.

  • Schema intelligence: Our pipelines include automated schema inference and evolution to prevent breaking downstream analytics.

  • Data enrichment: We move data through automated cleansing and enrichment stages to prepare it for immediate use in machine learning and decisioning.

04

Advanced analytics

Intelligence that goes beyond the dashboard

  • Predictive workflows: We build machine learning models with feature preparation, hyperparameter tuning, and experiment tracking.

  • Decision-driven BI: We design executive dashboards and self-service portals that provide narrative insights and storytelling, not just charts.

  • Model governance: Every model is deployed with active monitoring for prediction accuracy, drift detection, latency, and bias.

  • Continuous optimization: We implement feedback loops for automated retraining, ensuring your insights stay accurate as your business evolves.

What our clients will see

Reduction in mean time to resolution

Reduction time to resolution

30-40%

Average ROI

99.9%

Efficiency gains

60%+

Improvement in data accuracy

90%+

90%

Why companies choose Tenjumps

Senior engineers from day one, not after the sale

The team that designs your architecture is the same team that builds your pipelines. No junior bench-padding, no handoff to an offshore team you have never met. Our pods are led by engineers with 30+ years of individual experience who stay on your engagement from assessment through production and knowledge transfer.

Modern lakehouse architecture, multi-cloud by default

We build on the Databricks Lakehouse platform because it unifies data engineering, analytics, ML, and governance in one environment. But we are not locked to a single vendor or cloud. Every architecture we design works across AWS, Azure, and GCP, so your platform decisions serve your business goals, not a partner relationship.

Built to hand off, not to lock in

Every engagement includes knowledge transfer, governance runbooks, and team enablement. We build onboarding checklists, training programs, and self-service frameworks so your internal data engineers, analysts, and security teams can own the platform independently after we leave.

Success stories

Results that speak for themselves

60%

of tickets resolved instantly

Customer service automation

The Challenge: A logistics leader was overwhelmed by 150+ daily emails—83% of which were repetitive shipping queries.

The Solution: Tenjumps deployed an AI chatbot trained on historic email patterns in just 60 days.

The Result: 60% of tickets resolved automatically without human intervention.

  • 24/7 global support across 200+ countries.

  • CS reps redirected to high-value, complex cases.

99%

reduction in candidate verification time

HR automation

The Challenge: A financial services firm had a 4-month hiring lag due to manual recruiter verification.

The Solution: We built an agentic AI solution in only 10 days to automate re-engagement and LinkedIn verification.

The Result: 70% candidate re-engagement with 90% matching accuracy.

  • Delivery time slashed from 4 months to 4 weeks.

  • Eliminated weeks of manual searching for the team.

Featured

Read our latest insights on data engineering

How we evaluate, deploy, and govern AI with your team.

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How we work

From assessment to production in four stages

Our Business Excellence Model (BEM) takes you from where you are today to a modern, governed data platform. One team owns the entire engagement. No handoffs between strategy consultants and engineering teams.

01

Explore

Strategy & Readiness

We audit your data foundation and infrastructure to identify high-value use cases. The output is a prioritized roadmap based on technical feasibility and business ROI.

02

Engage

Architecture & Governance

We select the right tech stack—RAG, agents, or ML—and design for scale. For regulated industries, we bake in compliance frameworks and guardrails before a single line of code is written.

03

Execute

Agile Deployment

Our engineering pods build and ship. Whether it’s GenAI agents, MLOps pipelines, or intelligent automation, we deploy with full observability, auditability, and governance from day one.

04

Evolve

Optimization & Autonomy

We monitor for drift, bias, and performance, building feedback loops for continuous retraining. Our goal is to mature your internal AI capability so you own the platform.

Related content

Insights from our team

Explore all insights

Data Quality

Cost of poor data quality

A single data quality issue cost 50 engineering hours last quarter. Only 6 were tracked. Paleti Lakshmikanth breaks down where the hidden time goes.

Data pipeline

Production data engineering: what tutorials never teach you

Production data engineering looks nothing like tutorials. Kavya Kumari shares what actually changes when pipelines run at scale and stakeholders are waiting.

Responsible data engineering

Cloud cost optimization: The 50GB report nobody reads

For the 50GB weekly export, 47 recipients receive it, but only 3 open it. Bhavya Venu breaks down how wasteful data exports drain cloud budgets and what to do about it.

FAQs about data engineering consulting

What data sources and platforms do you work with?

We ingest data from any source: databases, APIs, files, streaming platforms, and third-party SaaS applications. We support batch, real-time, and micro-batch ingestion through APIs, webhooks, CDC (using AWS DMS and Oracle GoldenGate), and file-based transfers. On the platform side, we build primarily on Databricks Lakehouse but work across AWS, Azure, and GCP, including Redshift, BigQuery, and Snowflake where needed.

We have legacy infrastructure (Hadoop, Teradata, on-prem databases). Can you migrate us?

Yes. We have a structured 6-stage migration approach that covers assessment and discovery, target architecture design, data and schema migration, pipeline and ETL redesign, BI and downstream integration, and parallel testing. We start with non-disruptive workloads, run legacy and new systems side by side, and only decommission the old platform once everything is performing optimally.

How do you handle data governance and compliance?

Governance is built into every engagement from the start. We implement Unity Catalog for centralized access control, data classification, lineage, and audit under a single control plane. For compliance, we support GDPR, HIPAA, and ILCS through PII detection, field-level masking and tokenization, encryption at rest and in transit, and attribute-based access controls. Our governance frameworks include runbooks, onboarding processes, and training so your internal teams can maintain compliance independently.

How long does a data engineering engagement take?

It depends on scope. An initial assessment typically takes 2-4 weeks. From there, we can have production pipelines running in 8-30 days for focused engagements. Large-scale migrations with parallel testing and BI cutover typically run 8-16 weeks. Our BEM delivery model is designed to show measurable progress in the first month regardless of total project scope.

Do you only work with Databricks?

We recommend and build on Databricks Lakehouse for most enterprise data engineering engagements because it unifies data engineering, analytics, ML, and governance in one platform. But we are cloud-agnostic and work across AWS, Azure, and GCP. If your existing infrastructure runs on Redshift, BigQuery, Snowflake, or other platforms, we assess what to migrate, what to keep, and what serves your business goals best.

How does Tenjumps pricing compare to larger firms?

Our teams are senior-only, which means a higher day rate than offshore-heavy firms but significantly fewer total days to production. Clients typically see production systems in 8-30 days versus the 3-6 month timelines common with larger firms. When you factor in total cost of engagement rather than rate card alone, our model consistently delivers better ROI, with mid-market clients averaging an 8-month payback period.

What happens after you deliver? Do you offer ongoing support?

Every engagement includes knowledge transfer, documentation, and team training so your internal team can operate independently. For organizations that want ongoing managed services, we offer operations pods that provide 24/7 monitoring, pipeline optimization, and continuous improvement. Our goal is to give you the choice, not to create a dependency.

Ready to build the data infrastructure your business deserves?

Whether you need to modernize legacy pipelines, migrate to a lakehouse, or stand up real-time analytics, we can show you what is possible in your first conversation.

Book a consultation