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Data Stacks Jobs (NOW HIRING)

Trinity Technology Solutions

Data Engineer

New York, NY · On-site

$125K - $150K/yr

Experienced in building ELT pipelines and working with modern data stacks. * Understanding of Data Lakehouse, Delta Lake, and columnar storage. * Experience with data governance, dbt, or CI/CD ...

Axle

Director of Data Solutions

Rockville, MD

Sets technical strategy and reference architecture across modern data stacks and cloud environments. * Leads cross-functional teams (data engineering, ML engineering, applied science, software ...

Axle

Director of Data Solutions

Rockville, MD

Sets technical strategy and reference architecture across modern data stacks and cloud environments. * Leads cross-functional teams (data engineering, ML engineering, applied science, software ...

Axle

Director of Data Solutions

Rockville, MD · On-site

Sets technical strategy and reference architecture across modern data stacks and cloud environments. * Leads cross-functional teams (data engineering, ML engineering, applied science, software ...

Anysphere, Inc

Data Platform Engineer

New York, NY · On-site +1

$125K - $150K/yr

You have built up modern data stacks at a low level, not just written jobs on top of it. * You have scaled performant ingestion of billions of data per day. * You're the go-to person for data ...

Cadence

Data Lead - APCM

Required : • 5+ years of experience working with SQL or other data querying languages • Hands-on experience with modern data stacks and tools such as Snowflake, Fivetran, and dbt (or equivalent ...

Charles Schwab Inc.

Data Engineering Lead

Westlake, TX · On-site

$157K - $205K/yr

You will work across a modern cloud data stack built on Snowflake and Google Cloud Platform (GCP to build scalable, resilient, and reusable platform capabilities. Key Responsibilities: Cloud-Native ...

Charles Schwab

Data Engineering Lead

Westlake, TX · On-site

$109K - $132K/yr

You will work across a modern cloud data stack built on Snowflake and Google Cloud Platform (GCP to build scalable, resilient, and reusable platform capabilities. Key Responsibilities: Cloud-Native ...

Inspire11

Data Engineer

Chicago, IL · On-site

$118K - $141K/yr

... stacks, such as Fabric, Databricks, Snowflake, Synapse, or Redshift. • Understanding of AI/ML concepts, and curiosity to explore their role in data automation and augmentation. • Experience with ...

phData

Program Manager, Data & AI

$53 - $71.75/hr

Join phData, a dynamic and innovative leader in the modern data stack. We partner with major cloud data platforms like Snowflake, AWS, Azure, GCP, Fivetran, Pinecone, Glean, and dbt to deliver ...

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All JobsData Stacks Jobs

Data Stacks information

What are Data Stacks?

Data stacks refer to the combination of tools, technologies, and frameworks used to collect, store, process, and analyze data within an organization. A typical data stack might include data ingestion tools, databases, data warehouses, processing frameworks, and analytics platforms. The purpose of a data stack is to streamline the flow of data from its source to actionable insights, supporting business intelligence and decision-making. Common examples include the Modern Data Stack, which often uses tools like Fivetran, Snowflake, dbt, and Looker.

What are the key skills and qualifications needed to thrive as a Data Engineer, and why are they important?

To thrive as a Data Engineer, you need strong skills in database design, data modeling, and programming languages like Python or Java, typically supported by a degree in computer science or a related field. Familiarity with tools such as SQL, Apache Spark, Hadoop, and cloud platforms like AWS or Google Cloud, as well as certifications in these technologies, is highly valued. Problem-solving abilities, attention to detail, and effective communication are important soft skills for this role. These skills are crucial for building reliable data pipelines, ensuring data quality, and supporting data-driven decision-making within organizations.

What is the difference between Data Stacks vs Data Analysts?

AspectData StacksData Analysts
Required CredentialsBachelor's in Computer Science, Data Science, or related fields; certifications like SQL, Python, or cloud platformsBachelor's in Statistics, Mathematics, or related fields; often certifications in Excel, SQL, or data visualization tools
Work EnvironmentTechnical teams, data engineering, software development environmentsBusiness units, reporting teams, data visualization platforms
Employer & Industry UsageTech companies, data-driven organizations, startupsFinance, marketing, healthcare, consulting firms

Data Stacks focus on building and managing the underlying data infrastructure, while Data Analysts interpret data to provide insights. Both roles require analytical skills, but Data Stacks professionals are more technical, working with data pipelines and databases, whereas Data Analysts focus on analyzing data to support decision-making.

What are some common challenges faced when managing modern data stacks, and how can they be addressed in this role?

Professionals managing modern data stacks often encounter challenges such as integrating diverse data sources, ensuring data quality, and maintaining system scalability as data volumes grow. Addressing these challenges typically involves collaborating closely with data engineers, analysts, and IT teams to implement robust data pipelines, automate data validation, and monitor system performance. Staying updated with evolving technologies and best practices is also crucial for proactive problem-solving and optimizing the data stack for business needs.
What cities are hiring for Data Stacks jobs? Cities with the most Data Stacks job openings:
What states have the most Data Stacks jobs? States with the most job openings for Data Stacks jobs include:
Data Stacks jobs near you

Data Engineer (DBT Specialist)

ACS Consultancy Services

Albany, NY • Remote

$117K - $140K/yr

Other

Posted 17 days ago

Job description

Job Title: Data Engineer (DBT Specialist)

Location: Remote

We are currently seeking candidates who meet the following qualifications.

Key Responsibilities

  • Design and implement scalable data models and transformation pipelines using DBT (Data Build Tool).
  • Build and maintain ELT workflows using tools like Airflow, Fivetran, or custom Python scripts.
  • Work with data warehouses such as Snowflake, BigQuery, or Redshift to optimize storage and performance.
  • Ensure data quality, governance, and consistency through testing, version control, and documentation in DBT.
  • Collaborate with analysts and stakeholders to understand data needs and deliver efficient, reusable models.
  • Monitor pipeline performance and troubleshoot issues across the data stack.
  • Implement and maintain CI/CD practices for analytics engineering.
  • Stay up-to-date with best practices in data modeling, pipeline architecture, and cloud data tools.
Requirements
  • Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or related field.
  • Experience in data engineering or analytics engineering roles.
  • Strong hands-on experience with DBT in production environments.
  • Solid understanding of SQL and data warehousing principles.
  • Experience with modern data stacks (e.g., Snowflake, BigQuery, Redshift).
  • Experience with Python or another scripting language for ETL/ELT tasks.
  • Experience with orchestration tools like Airflow, Prefect, or Dagster.
  • Git proficiency for version control and collaboration.
  • Strong communication and collaboration skills.
  • Federal Experience is a plus.
  • Required Security clearance.
    If you meet these qualifications, please submit your application via link provided in Linkedin.
    Kindly do not call the general line to submit your application.