Data Engineer (Azure Databricks Snowflake)

TekWissen LLC

Overland Park, KS

JOB DETAILS
SALARY
$46–$50.67
LOCATION
Overland Park, KS
POSTED
5 days ago
Overview:
TekWissen is a global workforce management provider headquartered in Ann Arbor, Michigan that offers strategic talent solutions to our clients world-wide. Our client provider of digital technology and transformation, information technology and services
Position: Data Engineer (Azure | Databricks | Snowflake)
Location: Frisco, TX and Overland Park, KS
Duration: 6 Months
Job Type: Temporary Assignment
Work Type: Hybrid
JOB SUMMARY:
  • Needed for Azure-native third party data enrichment platform using Databricks/Spark + Snowflake; focus on reliable governed pipelines, strong Spark troubleshooting, privacy/governance, and cost-aware engineering;
Team / Business Context:
  • You will join a data engineering team responsible for third party data enrichment augmenting first party datasets with external identity/attribute data to support analytics, activation, and research.
  • The enriched datasets are consumed by multiple downstream systems and teams, including the Customer Data Platform (CDP) and other analytics/research stakeholders.
  • The platform is Azure-native and built primarily on Databricks (processing + some ML workloads) and Snowflake (analytics/warehouse).
  • A major focus is building reliable, governed, vendor agnostic datasets while ensuring privacy/compliance, data governance, and cost efficiency.
Key Responsibilities
  • As a Data Engineer, you will: Data Ingestion & Pipeline Development Build and enhance ingestion pipelines for large batch and event-driven paths (streaming may evolve over time).
  • Integrate data from: Third party enrichment vendors (identity + attributes, very large volumes) Digital platforms via Conversion API (CAPI) integrations (through intermediary/middleware) Rewards/Promotions systems (e.g., TMT) for offer issuance/redemption/consumption data
  • Data Quality, Reliability & Operations Implement strong data validation, idempotency, replay/backfill strategies, and deduplication to prevent quality drift.
  • Own monitoring, alerting, dashboarding, and operational readiness ( wrappers around core pipelines).
  • Troubleshoot failures with root cause analysis not just reruns: Interpret Spark logs Diagnose performance issues (shuffle, skew, partitioning) Improve stability and SLA adherence Governance & Compliance (First-class NFR) Apply privacy, compliance, and governance requirements across pipelines and datasets.
  • Support governance standards such as: Unity Catalog, lineage, access controls Managing PII vs non PII access Documentation of tables, schemas, catalogs, and cluster usage
  • Cost Governance & Performance Optimization Design pipelines with cost awareness from day one: Cluster sizing, workload tuning, efficient compute/storage usage Trade-off decisions balancing cost vs quality vs SLA Collaboration & Ownership Work in a small, fast-moving team; be self-driven and ownership-oriented.
  • Raise and manage data quality escalations when issues are detected.
  • Contribute to evolving architecture (product is early-stage; first live month was recent).
Must-Have Skills (Screening Keywords)
  • Candidate with hands-on, recent experience in: Strong coding: PySpark + SQL (hands-on, not only orchestration)
  • Databricks: notebooks/jobs, performance tuning fundamentals, medallion patterns Spark fundamentals: partitioning, skew/shuffle optimization, understanding failures via logs
  • Snowflake: data modeling/usage for analytics/warehousing workloads Azure ecosystem: Azure Data Factory (ADF) (orchestration) Azure-native integrations and services exposure
  • Data engineering reliability patterns: validation, idempotency, replay/backfills, dedup, auditability Data governance: Unity Catalog (preferred), lineage, access control patterns, PII handling Ownership mindset: can execute independently without constant approvals/check-ins
Nice-to-Have Skills
  • Event-driven/streaming ingestion exposure (even if primary is batch today)
  • Delta/Databricks patterns such as Delta Live Tables (DLT) (some workflows exist)
  • Experience building config-driven export frameworks for multiple downstream consumers/vendors
  • Exposure/interest in identity resolution concepts (ML optional; ETL strength is priority)
  • Familiarity with CAPI integrations / marketing tech data signals
  • Experience implementing operational telemetry: dashboards, alerts, SLA monitoring
  • What Good Looks Like (Success Criteria) Ships reliable, well-governed datasets with strong data quality practices
  • Can scale pipelines for very large volumes (hundreds of millions of records per vendor)
  • Prevents silent failures where quality degrades without obvious job failures
  • Balances delivery speed with compliance, governance, and cost controls
TekWissen Group is an equal opportunity employer supporting workforce diversity.

About the Company

T

TekWissen LLC

WE THE TEKWISSEN PEOPLE

TekWissen offers you a broader portfolio of services, industry-leading solutions, and the meaningful innovations that give you greater flexibility and speed to respond to market dynamics, reduced costs and risk to improve enterprise performance, and increased productivity to enable growth.

To keep pace with global market demands, TekWissen keeps its finger on the pulse of change. Our organized approach to guiding a project from its inception to closure. Managing projects is becoming more and more important as we enter the digital era. To cope with the pace that this transition demands, a method is required to manage projects so they can yield quality work, while incorporating efficient use of time and resources.

Project involves identifying which quality standards are relevant to the project and determining how to satisfy them.

It is important to perform quality planning during the Planning Process and should be done alongside the other project planning processes because changes in the quality will likely require changes in the other planning processes, or the desired product quality may require a detailed risk analysis of an identified problem. It is important to remember that quality should be planned, designed, then built in, not added on after the fact.

Capabilities and accomplishments in one TekWissen business enhance the opportunity for success in the others. Put simply, TekWissen's unique combination of attributes promotes success.



COMPANY SIZE
100 to 499 employees
INDUSTRY
Computer/IT Services
FOUNDED
2009
WEBSITE
http://www.tekwissen.com/