AWS Data Engineering Services
Pipelines, lakes and warehouses on AWS, secured and automated.
Stack we work in
- Terraform
- Snowflake
- Databricks
dbt- Apache Spark
- Apache Airflow
- Python
- GitHub Actions
- Projects delivered
- 680+
- Clients served
- 310+
- Client retention
- 98%
- Certified engineers
- 20+
- Building since
- 2018
Figures as of Oct 2026
Trusted by
- Aralia
- Rosa Pistrina
- Lashbo
- Micromunchi
- Payroll Ninja
- POD
- Petdx
- Yokey
- Spin Up
- Starwear
- Udlins
- Vegpal
- Wander Wings
- Wish It
- Wisl
Signs your AWS data platform needs work
We design, build and run data platforms on Amazon Web Services.
Read the full overview
Our AWS data engineering services cover data lakes on Amazon S3 with AWS Glue and Lake Formation, warehouses on Amazon Redshift, streaming with Amazon Kinesis and Amazon MSK, and ad hoc querying with Amazon Athena. We build batch and streaming ETL and ELT pipelines, replicate databases with AWS DMS, run Spark workloads on Amazon EMR and orchestrate jobs with Step Functions and Lambda. Where Snowflake or Databricks run on AWS, we connect them to the same lake, security model and dbt transformations. IAM, encryption and CloudWatch monitoring keep the platform secure and visible, and everything is defined in Terraform or CloudFormation.
Most AWS data estates grow one job at a time. These are the symptoms of growth without a plan.
Jobs built in the console
Glue jobs and Lambda functions are created and changed in the console, with no code review and no way to roll back.
Long-lived access keys
Pipelines authenticate with IAM user keys kept in scripts or environment variables instead of roles.
A lake nobody can query
Files land in S3 as many small CSV or JSON objects with no partitions, catalog entries or table format.
Full reloads every night
Every table is copied in full because there is no incremental load or change data capture.
Bills nobody can explain
Athena scans whole buckets, clusters run idle and no one owns the cost alerts.
Outcomes
What you get
- Glue, DMS and Kinesis pipelines that load only new and changed data
- An S3 data lake and Redshift warehouse organized for analytics
- Access through IAM roles and Lake Formation, with no keys in code
- Every resource defined in Terraform or CloudFormation and deployed through CI/CD
- CloudWatch alarms and cost alerts that surface problems before users notice
What we deliver
What you receive from an AWS engagement
Data lake foundation
S3 buckets per layer, the Glue Data Catalog, partitioning and Iceberg tables, with naming and tagging aligned with your account structure.
Configuration-driven ingestion
Glue, DMS and Kinesis pipelines where adding a source means adding configuration, not writing a new job.
Access and encryption
IAM roles, Lake Formation permissions, KMS keys and Secrets Manager for every credential.
Infrastructure and release pipelines
Terraform or CloudFormation, deployed through CI/CD across development, test and production.
Monitoring and budgets
CloudWatch alarms for failed runs and late data, plus AWS Budgets alerts per account.
Platform documentation
Architecture diagrams, data flows and runbooks your operations team can use on day one.
AWS data platform
- Data lakes on Amazon S3 with the AWS Glue Data Catalog
- Amazon Redshift data warehouse design and development
- Querying the lake with Amazon Athena and Redshift Spectrum
- Snowflake and Databricks on AWS
- Amazon RDS and Aurora integration
Built with
- Amazon S3
- AWS Glue
- AWS Glue Data Catalog
- Amazon Redshift
- Amazon Athena
- Amazon RDS
- Snowflake
- Databricks
Pipelines & streaming
- ETL and ELT pipelines with AWS Glue
- Streaming with Amazon Kinesis and Amazon MSK
- Database replication and CDC with AWS DMS
- Orchestration with AWS Step Functions and AWS Lambda
- Spark and big data processing on Amazon EMR
- dbt transformations on Redshift, Snowflake or Databricks
Built with
- AWS Glue
- Amazon EMR
- Amazon MSK
- AWS Step Functions
- AWS Lambda
- AWS DMS
- Snowflake
- Databricks
Security, DevOps & modernization
- Access control with AWS Lake Formation and IAM
- Infrastructure as code with Terraform and AWS CloudFormation
- CI/CD, monitoring and alerting with Amazon CloudWatch
- Migration of on-premises databases and warehouses to AWS
- Amazon QuickSight reporting on governed data
Built with
- AWS Lake Formation
- Amazon QuickSight
- Amazon CloudWatch
- Terraform
- AWS CloudFormation
- 01IngestBatch, CDC and streams
- AWS Glue
- AWS Glue Data Catalog
- Amazon Kinesis Data Streams
- 02StoreWarehouse and lakehouse
- Amazon Redshift
- Snowflake
- Databricks
- Apache Iceberg
- 03TransformModels and tests
- dbt
- Apache Spark
- Python
- 04OrchestrateScheduling, CI/CD, catalog
- Apache Airflow
- Terraform
Why teams choose Equitysoft for AWS data engineering
No long-lived keys
Access runs through IAM roles and Lake Formation, with secrets in AWS Secrets Manager, so there are no access keys in code.
Every resource as code
Terraform, CloudFormation or CDK with CI/CD, so environments can be reviewed, repeated and rebuilt.
Open formats, no lock-in
Apache Iceberg tables on S3 stay readable by Athena, Redshift, Snowflake and Databricks, so you can change engines later.
AWS and partner platforms together
As Snowflake and Databricks partners, we connect them to the same lake and security model on AWS. 50+ engineers, 20+ of them certified on cloud and data platforms, delivering since 2018.
How we work
Assess
We map sources, existing jobs, AWS accounts, costs and security requirements.
You receive AWS data platform assessment
Design
Lake layers, table formats, the warehouse or query engine, and the account and access model are agreed up front.
You receive AWS data architecture and security design
Secure
We set up IAM roles, Lake Formation permissions, KMS keys, VPC endpoints and Secrets Manager before any production data arrives.
You receive Identity, network and encryption design
Build
Glue, DMS, Kinesis and dbt pipelines are built as code and parameterized for each environment.
You receive Parameterized pipelines in source control
Automate
Releases run through CI/CD; orchestration, CloudWatch alarms and AWS Budgets alerts are configured.
You receive CI/CD pipelines, alarms and dashboards
Migrate and support
We move remaining on-premises workloads to AWS and support the platform in production.
You receive Migration plan and runbooks
Tech stack
Technologies we use
Data platforms
Pipelines and orchestration
- AWS Glue
- AWS Glue Data Catalog
- Amazon Kinesis Data Streams
- dbt
- Apache Spark
- Apache Airflow
Databases
Languages
Cloud and DevOps
Engagement models
How we can work together
Project-Based Delivery
A defined scope, timeline and deliverables, from design through go-live.
Fixed-Price Implementation Projects
An agreed price for a clearly scoped implementation, such as a migration or a new warehouse.
Contract and Subcontract Engineering
Our engineers work under your contract, or as a subcontractor to consulting firms and system integrators.
Dedicated Data and AI Engineers
Data engineers, data scientists and analysts who join your team and work in your tools and processes.
Remote Consulting and Development
Consultants and developers working remotely, with overlap in your working hours.
Architecture and Technical Advisory
Architecture reviews, platform choices and design guidance for your own team.
Production Support and Maintenance
Monitoring, incident handling and ongoing fixes for the pipelines, models and dashboards you run in production.
Proof of Concept and Assessments
A short, fixed-scope engagement to test an approach on your data, prove a model or plan a migration.
Questions, answered
Amazon S3, AWS Glue, Amazon Redshift, Amazon Athena, AWS Lake Formation, Amazon EMR, Amazon Kinesis, Amazon MSK, AWS DMS, Step Functions, Lambda, Amazon RDS and Aurora, and Amazon QuickSight, plus Snowflake, Databricks and dbt running on AWS.
Athena suits ad hoc queries over data in S3, billed per query. Redshift suits frequent, concurrent BI queries on modeled data. Snowflake or Databricks on AWS make sense when you need their features or already use them. We compare the options against your query patterns, team skills and running costs, and often pair Athena with one warehouse.
Yes. We use AWS DMS for CDC from operational databases, Glue job bookmarks for incremental file loads, and MERGE into Apache Iceberg or Redshift tables, so each run moves only new and changed data.
Pipelines run under IAM roles rather than access keys. Database passwords and API tokens live in AWS Secrets Manager, and data is encrypted with KMS keys.
Yes. We use Kinesis Data Streams or Amazon MSK for ingestion, with Lambda, Glue streaming jobs or Amazon Managed Service for Apache Flink for processing, and Data Firehose to land data in S3 or Redshift. The choice depends on latency, volume and whether you already run Kafka.
Yes. We move databases to Amazon RDS, Aurora or Redshift with AWS DMS and the Schema Conversion Tool, reconcile row counts and totals, and validate the data before cut-over.
Partitioned Parquet or Iceberg tables so Athena scans less, S3 lifecycle rules and Intelligent-Tiering, right-sized Glue jobs, Redshift Serverless or paused clusters for uneven workloads, Spot Instances on EMR, and AWS Budgets alerts per account.
Yes. We follow your AWS Organizations structure, service control policies, naming and tagging, and request changes through your normal approval process.
Often, yes. Iceberg tables on S3 add updates, deletes, schema changes and time travel to plain files, and Athena, Redshift, EMR, Snowflake and Databricks can all read them. That keeps your data open to more than one engine.
An assessment takes a few weeks. The first pipelines and lake layers usually follow soon after, and further sources are added in waves. Cross-account access, VPC endpoints and firewall changes often set the pace, so we raise them in week one.
It depends on scope. Defined work such as an assessment, a lake foundation or a first set of pipelines can be fixed price, and ongoing platform work suits time and materials or dedicated engineers. AWS usage is billed to your own account. We send a written estimate with its assumptions after a first call. As a guide, an assessment usually takes 2 to 3 weeks and costs $3,000 to $10,000, and a first production phase takes 6 to 10 weeks and costs $15,000 to $60,000 (USD).
After a first call under NDA, we run a platform assessment with read-only access to your AWS accounts. We need an owner for the data, a contact in your cloud or security team, and a list of priority sources. You receive a written estimate with its assumptions before any build.
More about our AWS Data Engineering
Working togetherHow we work with your team
Inside your accounts and guardrails
We work within your AWS Organizations structure, service control policies and change process, not around them.
Network and security teams early
VPC endpoints, firewall rules and cross-account access often have the longest lead time, so we raise them in week one.
Infrastructure as code
Every resource is defined in templates your cloud team can review like any other change.
Open formats
Parquet and Iceberg tables on S3 stay readable by Athena, Redshift, EMR, Snowflake and Databricks, so you are not tied to one engine.
Where AWS data engineering fits best
Financial services
Private connectivity through VPC endpoints, KMS keys you control and CloudTrail logs retained for audit.
Healthcare
Encryption at rest and in transit, access reviews and data kept in the AWS Regions you choose, in line with HIPAA, GDPR and local privacy rules.
Logistics
Shipment and telematics events through Kinesis or MSK, landed in S3 for near-real-time status reporting.
SaaS
Tenant-separated storage and pipelines that load each customer's data without mixing it with another's.
Talk to an AWS data engineer
Tell us about your AWS accounts and what you need your pipelines to do.