Data Pipelines & ETL/ELT
Batch and streaming pipelines that ingest, clean and transform data from your systems, with monitoring and retries built in.
Technology Services · Data Engineering & Analytics
Data you can trust,
ready when you need it.
Reliable data pipelines, warehouses and analytics built on Spark, Hadoop, SQL and Python, so your teams can make decisions on data they trust and your AI has something solid to learn from.
Overview
Analytics and AI are only as good as the data beneath them.
Most organizations are not short of data. They are short of data that is clean, connected and available where it is needed. Reports disagree, pipelines break quietly and analysts spend more time wrangling than analyzing.
Our data engineers build the foundations: pipelines that move and transform data reliably, warehouses and lakes that organize it, and analytics layers that serve the business. It is also the essential groundwork for any machine learning or AI initiative that follows.
What's included
From raw data to reliable insight.
Data Warehouses & Lakes
Well-modelled storage in cloud warehouses and data lakes that gives everyone a single, consistent view.
Big Data Processing
Spark and Hadoop-based processing for datasets too large or complex for conventional tools.
Analytics & Reporting
SQL-based analytics, dashboards and self-service reporting that put insight in the hands of business teams.
Data Quality & Governance
Validation, lineage and access controls that keep data accurate, documented and appropriately protected.
Preparing Data for AI
Feature-ready datasets and pipelines that provide machine learning and generative AI projects a solid start.
Who it's for
When data is the bottleneck.
Teams with conflicting reports
Organizations consolidating systems
Companies planning AI initiatives
Analysts buried in manual data prep
Healthcare & life sciences
Financial services
Why Tekleads
Engineering-grade data foundations.
We treat data pipelines like production software: tested, monitored and documented.
Software engineering rigor
Version control, testing and CI/CD applied to data work, so pipelines are dependable.
Connected to AI
We also build the machine learning and generative AI that consumes this data, so nothing is lost in handoff.
Platform-flexible
Experience across the major cloud and on-premise data platforms, building on what you already have.
Technology
The data toolkit.
How we work
How a data project runs.
Understand the data
We inventory sources, quality issues and the decisions the data needs to support.
Design the architecture
Target data model, pipelines and platform, sized for your needs and budget.
Build & validate
Implement pipelines with automated data-quality checks, and validate outputs against trusted figures.
Deliver & enable
Dashboards, documentation and training so teams can use the data confidently.
FAQs
Common questions
What is the difference between data engineering and data science?
Data engineering builds the pipelines and platforms that make data reliable and available. Data science uses that data to build models and analyses. They depend on each other, and we offer both.
Do we need a data warehouse or a data lake?
It depends on your data types, volumes and use cases. Many organizations benefit from a combination. We recommend the architecture that fits your requirements.
Can you work with our existing tools?
Yes. We build on the platforms and reporting tools you already use where they are a good fit, and advise where they are not.
Wrestling with your data?